New Zealand's Job Landscape: Industry Contribution to the Country and Current Job Opportunities in the Technology IndustryΒΆ

IntroductionΒΆ

New Zealand faces a number of economic and employment challenges in the current global economic environment. Uncertainty in global markets combined with domestic policy changes have increased the volatility of New Zealand's employment market. In order to gain a deeper understanding of these issues and their potential impacts, this report provides a detailed analysis of New Zealand's employment market.

The key focus areas are:

  1. GDP Trends and Industry Contributions: Analyzing the overall changes in New Zealand's GDP and identifying the top three contributing industries.
  2. Employment Opportunities and GDP Contribution: Investigating whether industries that contribute the most to GDP also provide the most job opportunities.
  3. Job Market Insights by Sector: Evaluating job opportunities across six main industries based on data from Careers.NZ.
  4. Auckland's Economic and Employment Performance: Assessing Auckland's role as New Zealand's largest city in terms of GDP and vacancy indices (AVIs) from 2010 to 2022.
  5. Unemployment and Economic Health: Exploring the correlation between unemployment rates, Auckland's AVIs, and GDP.
  6. Long-Term Job Creation in Auckland: Analyzing the trends in job creation and destruction in Auckland from 1999 to 2022.
  7. Salary and Employment Prospects in Technology Industry: Investigating the employment prospects and highest salary ranges in the manufacturing and technology industries, as well as for data analyst positions.

By examining these key factors, we hope to better understand the challenges New Zealand currently faces and the opportunities within the technology industry.

Datasets used:ΒΆ

  1. NZ Industries' Employment Growth Contributors, 2022-2023(API: https://rep.infometrics.co.nz/new-zealand/employment/growth-contributorsοΌ‰
  2. NZ Industries' GDP Growth Contributors, 2022-2023(API: https://rep.infometrics.co.nz/new-zealand/economy/growth-contributorsοΌ‰
  3. Job profiles(Web scraper: https://www.careers.govt.nz/searchresults?tab=jobsοΌ‰
  4. jobs-online-all.xlsx
  5. high_level_industry_GDP.xlsx
  6. pro_industry.csv
  7. industryGDP.csv
  8. regionjob.csv

Dataset sources:ΒΆ

  1. Infometrics(https://www.infometrics.co.nz)
  2. Careers.govt.nz
  3. Ministry of Business, Innovation and Employment (MBIE)
  4. Stas NZ (https://infoshare.stats.govt.nz/)

Research QuestionsΒΆ

  1. Discuss the overall GDP changes in New Zealand from 2010 to 2022. What are the top three industrial contributions?

  2. Does the industry that contributes the most GDP bring the same proportion of job opportunities?

  3. What are the job opportunities in the six main industries based on the occupation listed on Careers.NZ?

  4. As the largest city in New Zealand and a major economic hub, is Auckland the best in terms of all vacancy indices (AVIs) and GDP from 2010 to 2022?

  5. How does the unemployment rate in New Zealand relate to Auckland's AVI and GDP?

  6. Does the job creation in Auckland from 1999 to 2022 remain positive, making Auckland the city with the most economic contribution?

  7. What are the employment prospects and highest salary ranges for the manufacturing and technology industries, as well as for data analyst positions?

Executive SummaryΒΆ

Data Acquisition&WragglingΒΆ

Data Acquisition

  1. API retrieval of 'GDP contribution data and employment by New Zealand industry'.
  2. Web-scraped a list of 'job profiles' in New Zealand.
  3. Downloaded tabular data on the labour market and GDP dimensions from the relevant sources as static data sets.

Data Wraggling

  1. GDP contribution and employment by industry data: First, histograms were plotted to locate missing values. After cleaning the data, a bar chart was generated to examine the distribution.
  2. Job profile datasets: A cross-tabulation was created to count the number of job opportunities in each industry after web-scraping the data.
  3. Job online data: The names of the multi-index columns were processed, and after removing the missing observations, the time series plots were generated.
  4. Regional Employment Data: Bar charts were created for each variable to examine outlier values. Quarterly data variables were converted to annual variables, which involves grouping data by year and then summing them, and finally, the 'Type' column was pivoted to group the data into 'job creation' and 'job destruction' categories.
  5. Labour Force Status: Plot histograms and remove outliers. Finally, kernel density plots were used to check the data distribution.
  6. Median wage: Plotted boxes to examine the data distribution and then converted the data into annual form.
  7. GDP by region: Locate a missing value and perform data imputation. Growth rates from recent years were used to predict the missing values.
  8. GDP by industry: Locate missing values and perform data imputation. Create a heatmap to identify the variable most correlated with the missing value column. Then, build a regression model to predict the missing values

Data IntegrationΒΆ

  1. Preparation: Before merging different datasets, the structure and naming of each table are standardised to ensure consistency across data sources.
  2. Merge: A custom function was implemented to merge any number of datasets with a common key efficiently. This allows us to input all datasets simultaneously and merge them quickly.
  3. Big data processing optimisation: Finally, strategies were implemented to optimise the processing of big data, including using Dask DataFrame and HDF5 formats. This phase also included creating a custom function to quickly rename columns. mat:

AnalysisΒΆ

Using group-by, pivot tables, cross-tabulation, and visualization to answer each research question.

InΒ [1]:
# Import python libraries required for this report
import pandas as pd
import json
from time import sleep
from datetime import datetime
import numpy as np
import requests                
from bs4 import BeautifulSoup  
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib
import seaborn as sns
from pylab import rcParams
%matplotlib inline
matplotlib.style.use('ggplot')
import time  

1. Data Acquisition&WragglingΒΆ

In this section, the following data was acquired.

Dynamic datasets:

  1. API-sourced data: Contributions to GDP and employment by industry (based on the ANZSIC Level 1 industry classification standard) in New Zealand.
  2. Web-scraped data: Over 400 job profiles in New Zealand from careers.govt.nz, including information on salary, entry requirements and job prospects.

Static datasets:

  1. Job Online Data: All vacancy indices (AVIs) published by the Ministry of Business, Innovation and Employment (MBIE).
  2. High-level industry GDP contribution data: GDP contributions by primary, goods-producing and services industries.
  3. Professional, scientific and technical services industry data.
  4. Regional employment data.

1.1 Dynamic datasetsΒΆ

1.1.1 NZ Industry Contributions to GDP and EmploymentΒΆ

(1) What is the data
This dataset covers numerous industries in New Zealand classified to the ANZSIC Level 1 industry standard. The data cover two statistical periods, 2022 and 2023.

  • GDP contribution: defined as the annual GDP value generated by each industry, measured in millions.

  • Employment contribution: defined as the number of filled jobs within each industry for that year.

(2) How the data was obtained:

  • The data were obtained from https://rep.infometrics.co.nz/new-zealand/economy/growth-contributors.
  • The website does not provide an open API. However, we found that the data on the website is dynamically generated using JavaScript. By inspecting the page source, we identified the API endpoints corresponding to the relevant tables. Using these APIs, we successfully retrieved the raw data from the tables.

(3) What to do with this dataset

  • Check dataset integrity and clean data.
  • Extract relevant GDP and employment contribution columns from the original tables, then merge for further analysis.
InΒ [2]:
#The first dataset: GDP contribution

#URL:https://rep.infometrics.co.nz/new-zealand/economy/growth-contributors
#ANZSIC Level 1 industries' contribution to growth, 2022-2023

# Define URL for GDP data retrieval
url1 = 'https://production.infometrics.co.nz/api/rep/data/?series=GDP&year=2022&year=2023&breakdown=GROWTH&area_id=new-zealand&industry_type_code=L1&industry_type_code=TOTAL'

# Send GET request to retrieve GDP data
response1 = requests.get(url1)
data1 = response1.json()  

# Expanding nested data with json_normalize
industryGDP_series = pd.json_normalize(data1, 'series', errors='ignore')

industryGDP_values = pd.json_normalize(data1, record_path=['series', 'values'], 
                              meta=['key', ['industry_types', 'industry_type_code'], ['industry_types', 'name']],
                              errors='ignore')

industryGDP_values.head()
Out[2]:
change_contribution change change_total_val area_name year_base year_focus value_base value_focus area_id area_type ... industry_type_code industry industry_short industry_description percent_total parent_industry_code parents key industry_types.industry_type_code industry_types.name
0 2.80 2.8 10201.2 New Zealand 2022 2023 367582.4 377783.6 new-zealand NZ ... TOTAL total economy total For data not broken down by industry NaN NaN NaN NaN NaN gross domestic product
1 0.15 3.0 549.0 New Zealand 2022 2023 18182.6 18731.6 new-zealand NZ ... L1 agriculture, forestry and fishing agriculture, forestry, fishing Includes: growing crops, raising animals, grow... 4.96 TOTAL [{'industry_type_code': 'TOTAL', 'industry_cod... NaN NaN gross domestic product
2 -0.06 -7.1 -216.2 New Zealand 2022 2023 3052.9 2836.7 new-zealand NZ ... L1 mining mining Includes: extraction of naturally occurring mi... 0.75 TOTAL [{'industry_type_code': 'TOTAL', 'industry_cod... NaN NaN gross domestic product
3 -0.55 -6.0 -1988.5 New Zealand 2022 2023 32875.9 30887.4 new-zealand NZ ... L1 manufacturing manufacturing Includes: physical or chemical transformation ... 8.18 TOTAL [{'industry_type_code': 'TOTAL', 'industry_cod... NaN NaN gross domestic product
4 0.07 2.7 260.9 New Zealand 2022 2023 9739.0 9999.9 new-zealand NZ ... L1 electricity, gas, water and waste services electricity, gas, water, waste Includes: provision of electricity, gas throug... 2.65 TOTAL [{'industry_type_code': 'TOTAL', 'industry_cod... NaN NaN gross domestic product

5 rows Γ— 22 columns

InΒ [3]:
# Select the specified column and create a copy
industryGDP = industryGDP_values[['industry_short', 'value_base', 'value_focus']].copy()

industryGDP.head()
Out[3]:
industry_short value_base value_focus
0 total 367582.4 377783.6
1 agriculture, forestry, fishing 18182.6 18731.6
2 mining 3052.9 2836.7
3 manufacturing 32875.9 30887.4
4 electricity, gas, water, waste 9739.0 9999.9

This dataset contains data from the years 2022 and 2023, with 'value_base' and 'value_focus' representing the data for each year. We will perform checks on these two variables and change the column names to improve the clarity of the table.

InΒ [4]:
# Rename Columns
industryGDP.rename(columns={
   'industry_short': 'Industry',
   'value_base': 'GDP_2022(M)',
   'value_focus': 'GDP_2023(M)',
   }, inplace=True)

# Setting the graph size
plt.figure(figsize=(10, 4))

# Use index as the x-axis coordinate
index_labels = range(len(industryGDP))

# Plot histograms of both variables separately
plt.subplot(1, 2, 1)  
plt.bar(index_labels, industryGDP['GDP_2022(M)'], color='blue', alpha=0.7)
plt.title('GDP_Contribution_2022')
plt.xlabel('Index')
plt.ylabel('Value')
plt.xticks(index_labels)  

plt.subplot(1, 2, 2)
plt.bar(index_labels, industryGDP['GDP_2023(M)'], color='green', alpha=0.7)
plt.title('GDP_Contribution_2023')
plt.xlabel('Index')
plt.ylabel('Value')
plt.xticks(index_labels)  

plt.tight_layout()


plt.show()
No description has been provided for this image

From the graph it's clear that the data at index 0 is an outlier, corresponding to the 'total' row in the dataset. All the other data appears normal, so we only need to remove the 'total' row.

InΒ [5]:
industryGDP = industryGDP.drop(0)
industryGDP.head()
Out[5]:
Industry GDP_2022(M) GDP_2023(M)
1 agriculture, forestry, fishing 18182.6 18731.6
2 mining 3052.9 2836.7
3 manufacturing 32875.9 30887.4
4 electricity, gas, water, waste 9739.0 9999.9
5 construction 23404.8 23905.0

The next step is to obtain data on the employment contribution of each industry.

InΒ [6]:
#The second dataset: Employment contribution

#Contribution to employment by industry: 2023 vs. 2022
#https://rep.infometrics.co.nz/new-zealand/employment/growth-contributors
url2 = 'https://production.infometrics.co.nz/api/rep/data/?series=EMP_FILLED&year=2022&year=2023&breakdown=GROWTH&area_id=new-zealand&industry_type_code=L1'

response2 = requests.get(url2)
data2 = response2.json()  

# Expanding nested data with json_normalize
industryEmployment_series = pd.json_normalize(data2, 'series', errors='ignore')

industryEmployment_values = pd.json_normalize(data2, record_path=['series', 'values'], 
                              meta=['key', ['industry_types', 'industry_type_code'], ['industry_types', 'name']],
                              errors='ignore')




industryEmployment_values.head()
Out[6]:
percent_total industry_type_code industry_code industry industry_short industry_description change_contribution change change_total_val area_name year_base year_focus value_base value_focus area_id area_type sort_order key industry_types.industry_type_code industry_types.name
0 5.29 L1 A agriculture, forestry and fishing agriculture, forestry, fishing Includes: growing crops, raising animals, grow... -0.05 -0.8 -1248.0 New Zealand 2022 2023 147044.0 145796.0 new-zealand NZ 9999 NaN NaN employment
1 0.22 L1 B mining mining Includes: extraction of naturally occurring mi... 0.01 3.9 230.0 New Zealand 2022 2023 5932.0 6162.0 new-zealand NZ 9999 NaN NaN employment
2 9.15 L1 C manufacturing manufacturing Includes: physical or chemical transformation ... 0.21 2.2 5451.0 New Zealand 2022 2023 246399.0 251850.0 new-zealand NZ 9999 NaN NaN employment
3 0.76 L1 D electricity, gas, water and waste services electricity, gas, water, waste Includes: provision of electricity, gas throug... 0.04 5.7 1135.0 New Zealand 2022 2023 19816.0 20951.0 new-zealand NZ 9999 NaN NaN employment
4 10.45 L1 E construction construction Includes: construction of buildings and other ... 0.35 3.3 9314.0 New Zealand 2022 2023 278338.0 287652.0 new-zealand NZ 9999 NaN NaN employment
InΒ [7]:
# Select the specified column and create a copy
industryEmployment = industryEmployment_values[['industry_short', 'value_base', 'value_focus']].copy()

industryEmployment.rename(columns={
   'industry_short': 'Industry',
   'value_base': 'Employment_2022',
   'value_focus': 'Employment_2023',
   }, inplace=True)

# Setting the graph size
plt.figure(figsize=(10, 4))

# Use index as the x-axis coordinate
index_labels = range(len(industryEmployment))

# Plot histograms of both variables separately
plt.subplot(1, 2, 1)  
plt.bar(index_labels, industryEmployment['Employment_2022'], color='blue', alpha=0.7)
plt.title('GDP_Contribution_2022')
plt.xlabel('Index')
plt.ylabel('Value')
plt.xticks(index_labels)  

plt.subplot(1, 2, 2)
plt.bar(index_labels, industryEmployment['Employment_2023'], color='green', alpha=0.7)
plt.title('GDP_Contribution_2023')
plt.xlabel('Index')
plt.ylabel('Value')
plt.xticks(index_labels)  

plt.tight_layout()


plt.show()
No description has been provided for this image

From the analysis of the above figure it can be seen that the observations indexed 1 and 3 are at lower values, which could be a cause for concern.
To further understand this situation, it is necessary to identify which industry these observations belong.

InΒ [8]:
print(industryEmployment.iloc[1]['Industry'])
print(industryEmployment.iloc[3]['Industry'])
mining
electricity, gas, water, waste

Looking at the data, it can be confirmed that the industry with index 1 is 'Mining', while the industry with index 3 is 'Electricity, gas, water supply and waste management'.
In many countries, these sectors, which are essential for economic stability, do not usually generate significant employment due to the high degree of automation and mechanisation. In this context, the low figures are to be expected and should not be regarded as outliers.
Therefore, no special treatment is required in our data analysis.

For further analysis, the data on each industry's contribution to GDP and employment are then merged in a table.

InΒ [9]:
# Merge two DataFrames based on Industry columns
merged_industrycontribution = pd.merge(industryEmployment, industryGDP, on='Industry')

merged_industrycontribution.head()
Out[9]:
Industry Employment_2022 Employment_2023 GDP_2022(M) GDP_2023(M)
0 agriculture, forestry, fishing 147044.0 145796.0 18182.6 18731.6
1 mining 5932.0 6162.0 3052.9 2836.7
2 manufacturing 246399.0 251850.0 32875.9 30887.4
3 electricity, gas, water, waste 19816.0 20951.0 9739.0 9999.9
4 construction 278338.0 287652.0 23404.8 23905.0
InΒ [10]:
color1 = 'lightblue'
color2 = 'salmon'

fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(16, 8))  

# the first figure:GDP Contribution by Industry for 2022 and 2023
bar_height = 0.35
opacity = 0.8

index1 = range(len(merged_industrycontribution['Industry']))

bar1_1 = axes[0].barh(index1, merged_industrycontribution['GDP_2022(M)'], bar_height, alpha=opacity, color=color1, label='GDP_2022(M)')
bar1_2 = axes[0].barh([p + bar_height for p in index1], merged_industrycontribution['GDP_2023(M)'], bar_height, alpha=opacity, color=color2, label='GDP_2023(M)')

axes[0].set_ylabel('')
axes[0].set_xlabel('GDP Contribution (Millions)')
axes[0].set_title('GDP Contribution by Industry for 2022 and 2023')
axes[0].set_yticks([p + bar_height / 2 for p in index1])
axes[0].set_yticklabels(merged_industrycontribution['Industry'])
axes[0].legend()

# the second:Ely Contribution by Industry for 2022 and 2023
index2 = range(len(merged_industrycontribution['Industry']))

bar2_1 = axes[1].barh(index2, merged_industrycontribution['Employment_2022'], bar_height, alpha=opacity, color=color1, label='Employment_2022')
bar2_2 = axes[1].barh([p + bar_height for p in index2], merged_industrycontribution['Employment_2023'], bar_height, alpha=opacity, color=color2, label='Employment_2023')

axes[1].set_ylabel('')
axes[1].set_xlabel('Employment Contribution (Millions)')
axes[1].set_title('Employment Contribution by Industry for 2022 and 2023')
axes[1].set_yticks([p + bar_height / 2 for p in index2])
axes[1].set_yticklabels(merged_industrycontribution['Industry'])
axes[1].legend()

plt.tight_layout()
plt.show()
No description has been provided for this image

We can identify several key points from the chart:

  1. The 'Professional, scientific and technical services' sector shows a significant growth trend, both in terms of its contribution to GDP and employment. This suggests that this sector is playing an increasingly important role in the New Zealand economy, probably due to technological innovation, increased demand and policy support.

  2. Although the manufacturing sector's contribution to GDP is declining, its contribution to employment is increasing. This may indicate that the manufacturing sector is undergoing structural adjustment, or that automation technology has not yet fully replaced manual labour. It may also reflect government measures to protect jobs.

  3. Some sectors, such as wholesale and retail trade, have relatively small contributions to GDP but relatively large contributions to employment. This suggests that these sectors are likely to be labour intensive and important in supporting the labour market, although the economic value of individual jobs in these sectors is relatively low.t trade.

1.1.2 Job profiles datasetsΒΆ

(1) What is the data
The Job Profiles datasets are a comprehensive database provided by Careers.govt.nz that provides detailed information on various occupations, including job titles, salary levels, required skills and training duration. These profiles are based on research and analysis of the New Zealand labour market and aim to provide comprehensive career information for job seekers, students and career planners.

(2) How the data was obtained:
Data crawled from: https://www.careers.govt.nz/searchresults?tab=jobs.

  • As the career information is spread over several pages, the page-flipping crawling is implemented using loops and page parameters. In each loop, the program calculates the starting position of each page, constructs the URL and sends a request to retrieve the page content.
  • At the end of each loop, the program adds a 1 second delay to avoid overloading the server.
  • Then, modules containing career information were extracted from each page and parsed for key information such as title, subtitle, description, employment opportunities and training required.
  • Finally, the extracted data is organised into a list and then converted into a DataFrame for subsequent analysis.

(3) What to do with this dataset

Check dataset integrity and clean data. Extract relevant GDP and employment contribution columns from the original tables, then merge for further analysis.

InΒ [11]:
# URL
base_url = "https://www.careers.govt.nz/searchresults?tab=jobs"
total_pages = 42  # Total pages

data = []

for page in range(total_pages):
    # Calculate the start position of each page
    start_index = page * 10
    
    # Building URLs for paging
    url = f"{base_url}&start={start_index}"
    
    # Request page
    response = requests.get(url)
    html_content = response.text
    
    # Parsing HTML
    soup = BeautifulSoup(html_content, 'html.parser')
    
    # Iterate over all article tags
    for article in soup.find_all('article', class_='careers-card job'):
        title = article.find('h3', class_='title').text.strip()
        sub_title = article.find('p', class_='sub-title').text.strip()
        description = article.find('div', class_='description').p.text.strip()
        job_opportunities = article.find('dl', class_='job-opportunities').dd.text.strip()
        training_required = article.find('dl', class_='training').dd.text.strip() if article.find('dl', class_='training') else 'N/A'
        vocations = [li['title'] for li in article.find_all('li')]
        earnings = [span.text.strip() for span in article.find_all('span', class_='amount')]

        # Organising data
        data.append({
            'Title': title,
            'Sub Title': sub_title,
            'Description': description,
            'Job Opportunities': job_opportunities,
            'Training Required': training_required,
            'Vocations': ", ".join(vocations),
            'Earnings': ", ".join(earnings)
        })
    
    # 1 second delay after each cycle
    time.sleep(1)

# Create a DataFrame
df_jobprofiles = pd.DataFrame(data)

df_jobprofiles.head()
Out[11]:
Title Sub Title Description Job Opportunities Training Required Vocations Earnings
0 Energy/​Carbon Auditor Kaitātari PΕ«ngao/​Waro Energy/carbon auditors assess the amount of en... Average 2-3 years Construction and infrastructure, Services indu... $60K-$80K per year, $80K-$200K per year
1 Television Presenter Kaipānui Pouaka Whakaata Television presenters introduce, present or ho... Poor N/A Creative industries
2 Facilities Manager Kaiwhakahaere Whakaurunga Facilities managers co-ordinate the strategic ... Good N/A Construction and infrastructure $55K-$100K per year, $100K-$150K per year
3 Aircraft Refueller KaiwhakakΔ« Waka Rererangi Aircraft refuellers fill aircraft with fuel at... Poor <1 year Services industries $55K-$65K per year, $65K-$75K per year
4 Electronics Engineer Mataaro Tāhiko Electronics engineers design and oversee produ... Good 4 years Manufacturing and technology $100K per year
InΒ [12]:
# descriptive statistics of the dataset
df_jobprofiles.describe()
Out[12]:
Title Sub Title Description Job Opportunities Training Required Vocations Earnings
count 417 417 417 417 417 417 417
unique 417 414 417 3 39 36 318
top Energy/​Carbon Auditor Ringawera Energy/carbon auditors assess the amount of en... Good N/A Services industries
freq 1 2 1 228 146 99 26
InΒ [13]:
# Rename Columns
df_jobprofiles.rename(columns={
   'Vocations': 'Industry',
   'Title': 'Occupation',
   }, inplace=True)

Based on the statistics in the table above, it can be found:

  • 'Title' column: with 417 unique values, the title of each job is unique, indicating that our dataset has a high degree of diversity in terms of job types.
  • 'Job opportunities' column: 'Good' appears 228 times, which is the most common rating for job opportunities. This suggests that many occupations have good job prospects in the market.
  • 'Training required' column: 'N/A' appears 146 times. This indicates that a large number of occupation entries don't list the required training requirements, which may affect the comprehensiveness of the occupations assessed.
  • 'Earnings' column: There were 26 records where no specific earnings information was provided.

To ensure the completeness and reliability of the data analysis, it is important to select columns that do not have null values.

InΒ [14]:
jobprofiles = df_jobprofiles[['Occupation', 'Job Opportunities','Industry']].copy()

jobprofiles.head()
Out[14]:
Occupation Job Opportunities Industry
0 Energy/​Carbon Auditor Average Construction and infrastructure, Services indu...
1 Television Presenter Poor Creative industries
2 Facilities Manager Good Construction and infrastructure
3 Aircraft Refueller Poor Services industries
4 Electronics Engineer Good Manufacturing and technology

For the table provided, it is necessary to count the number of job vacancies for each industry.

  • First of all, it's noticed that some values in the column 'Industry' contain several categories of industries. This requires us to split each industry information and rebuild the list. Specifically, we use the .str.split(', ') method to split the string in each cell by commas into lists, and treat each list as a new row.

  • Next, we use the pd.crosstab() method to create a cross-tabulation, taking the Industry column and the Job Opportunities column as parameters. This will calculate the frequency of each industry in relation to each job opportunity and store the result in a new DataFrame.

  • Finally, by summing the columns in the cross-tabulation, we calculate the total number of each job opportunity for further analysis and visualisation.

InΒ [15]:
# Split multiple occupational categories in the β€˜Vocations’ column and then flatten the multi-category structure.
jobprofiles['Industry'] = jobprofiles['Industry'].str.split(', ')
jobprofiles = jobprofiles.explode('Industry').reset_index(drop=True)

# Creating a Crosstab with crosstab
summary_table = pd.crosstab(jobprofiles['Industry'], jobprofiles['Job Opportunities'])

# Calculate the total number of ratings for each job opportunity
total_job_opportunities = summary_table.sum()

summary_table
Out[15]:
Job Opportunities Average Good Poor
Industry
Construction and infrastructure 16 40 3
Creative industries 22 7 29
Manufacturing and technology 25 64 11
Primary industries 22 34 3
Services industries 61 75 30
Social and community services 29 66 15

1.2 Static datasetsΒΆ

Focusing on the labour market and the economic climate, a wide range of data was gathered.

  1. Job market
  • Online job data: All Vacancies Indices (AVI) published by the Ministry of Business, Innovation and Employment (MBIE) covering the period 2010 to 2024. This dataset includes various dimensions such as industry, occupation, region and skills.
  • Regional employment data: Quarterly data from 1999 to 2022, providing employment statistics for different regions and industries in New Zealand, including job creation and destruction.
  • Labour Force Status datasets: including measures such as employment, unemployment and participation rates.
  • Median Wage: Distribution of earnings across different age groups
  1. Economic climate:
    GDP is a key indicator of economic activity and performance. Two key datasets have been selected
  • GDP by region: provides insight into regional economic performance
  • GDP by industry: used to assess the sectoral contribution to overall economic growth.

1.2.1 Job marketΒΆ

1.2.1.1 Job online datasetΒΆ

Jobs Online provides a key indicator for assessing labour demand by tracking fluctuations in online job postings across four prominent internet job platforms: SEEK, Trade Me Jobs, the Education Gazette and Kiwi Health Jobs.The data can be used as a proxy indicator for job vacancies and can provide insight into trends across industries, occupations, skill levels and geographic regions.

InΒ [16]:
# import data
file_path = "../AAA/datasets/jobs-online-all.xlsx"

 # specify the first two rows as a multilevel column index
df_jobonline_all = pd.read_excel(file_path, header=[0, 1]) 

df_jobonline_all.head()
Out[16]:
Date Auckland Bay of Plenty Canterbury Gisborne Hawke's Bay Marlborough/NelsonTasman/West Coast Manawatu/Wanganui-Taranaki Northland Otago Southland Waikato ... OCCUPATION
Unnamed: 0_level_1 Unnamed: 1_level_1 Unnamed: 2_level_1 Unnamed: 3_level_1 Unnamed: 4_level_1 Unnamed: 5_level_1 Unnamed: 6_level_1 Unnamed: 7_level_1 Unnamed: 8_level_1 Unnamed: 9_level_1 ... Sales Machinery Drivers Labourers highly skilled skilled semi-skilled low skilled unskilled Skilled Unskilled
0 NaT NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 NaT NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 2009-09-01 NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 2009-12-01 NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
4 2010-03-01 NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

5 rows Γ— 36 columns

InΒ [17]:
#View all column names
columns = df_jobonline_all.columns.tolist()

print("Columns in the dataset:", columns)
Columns in the dataset: [('Date', 'Unnamed: 0_level_1'), ('Auckland', 'Unnamed: 1_level_1'), ('Bay of Plenty', 'Unnamed: 2_level_1'), ('Canterbury', 'Unnamed: 3_level_1'), ("Gisborne Hawke's Bay", 'Unnamed: 4_level_1'), ('Marlborough/NelsonTasman/West Coast', 'Unnamed: 5_level_1'), ('Manawatu/Wanganui-Taranaki', 'Unnamed: 6_level_1'), ('Northland', 'Unnamed: 7_level_1'), ('Otago Southland', 'Unnamed: 8_level_1'), ('Waikato', 'Unnamed: 9_level_1'), ('Wellington', 'Unnamed: 10_level_1'), ('Industry', 'Accounting'), ('Industry', 'Construction'), ('Industry', 'Education'), ('Industry', 'Health'), ('Industry', 'Hospitalty'), ('Industry', 'IT'), ('Industry', 'Manufacturing'), ('Industry', 'Primary'), ('Industry', 'Sales'), ('Industry', 'Other'), ('OCCUPATION', 'Managers'), ('OCCUPATION', 'Professionals'), ('OCCUPATION', 'Tech & Trades'), ('OCCUPATION', 'Community & Personal Services'), ('OCCUPATION', 'Clerical & Administration'), ('OCCUPATION', 'Sales'), ('OCCUPATION', 'Machinery Drivers'), ('OCCUPATION', 'Labourers'), ('OCCUPATION', 'highly skilled'), ('OCCUPATION', 'skilled'), ('OCCUPATION', 'semi-skilled'), ('OCCUPATION', 'low skilled'), ('OCCUPATION', 'unskilled'), ('OCCUPATION', 'Skilled'), ('OCCUPATION', 'Unskilled')]

The column names in this dataset have multi-level indexes and therefore need to be adjusted.

  1. For column names related to regions, only the name of each region will be kept and redundant rows will be removed.
  2. For column names related to industries, if the first part of the column name is β€˜Industry’, the second part of the column name will be added to a new list of column names. In addition, the prefix β€˜ids_’ will be added to indicate that these are industry-related columns.
  3. For occupation-related columns, if the first part of the column name is β€˜OCCUPATION’, the second part of the column name will be added to a new list of column names. Again, the prefix β€˜ocp_’ will be added to indicate that these are occupation-related columns.
InΒ [18]:
# Processing column names
new_columns = []
for col in df_jobonline_all.columns:
    # Check and process region names
    if "Unnamed" in str(col[1]):
        new_columns.append(col[0])
    # Dealing with Industry-related columns
    elif col[0] == 'Industry':
        new_columns.append(f'ids_{col[1]}')
    # Dealing with Occupation-related columns
    elif col[0] == 'OCCUPATION':
        new_columns.append(f'ocp_{col[1]}')
    else:
        new_columns.append(col)

# update column names
df_jobonline_all.columns = new_columns

print("Updated columns:", df_jobonline_all.columns)
Updated columns: Index(['Date', 'Auckland', 'Bay of Plenty', 'Canterbury',
       'Gisborne Hawke's Bay', 'Marlborough/NelsonTasman/West Coast',
       'Manawatu/Wanganui-Taranaki', 'Northland', 'Otago Southland', 'Waikato',
       'Wellington', 'ids_Accounting', 'ids_Construction', 'ids_Education',
       'ids_Health', 'ids_Hospitalty', 'ids_IT', 'ids_Manufacturing',
       'ids_Primary', 'ids_Sales', 'ids_Other', 'ocp_Managers',
       'ocp_Professionals', 'ocp_Tech & Trades',
       'ocp_Community & Personal Services', 'ocp_Clerical & Administration',
       'ocp_Sales', 'ocp_Machinery Drivers', 'ocp_Labourers',
       'ocp_highly skilled', 'ocp_skilled', 'ocp_semi-skilled',
       'ocp_low skilled', 'ocp_unskilled', 'ocp_Skilled', 'ocp_Unskilled'],
      dtype='object')

As can be seen from the above results, the column names have been successfully updated.
Since it is observed that there are blank rows at the beginning of this data frame, it is necessary to check and address any missing values in this data set.
The next step was to count the number of missing values in each row.

InΒ [19]:
from IPython.display import display, HTML

# Calculate the number of missing values for each row
missing_counts = df_jobonline_all.isna().sum(axis=1)

# Calculate the total number of columns in each row
total_columns = df_jobonline_all.shape[1]

# Calculate the percentage of missing values in each row
missing_percentage = (missing_counts / total_columns) * 100

# Create a new DataFrame showing the number and percentage of missing values in each row
missing_stats = pd.DataFrame({
    'Missing Values Count': missing_counts,
    'Missing Percentage': missing_percentage
})

# Setting up display configurations to avoid content being omitted
pd.set_option('display.max_rows', None)  
pd.set_option('display.max_columns', None) 
pd.set_option('display.width', None)  
pd.set_option('display.max_colwidth', None)  

# Use IPython's display function with HTML to create a scrolling viewframe to display the DataFrame.
display(HTML(missing_stats.to_html()))
Missing Values Count Missing Percentage
0 36 100.000000
1 36 100.000000
2 35 97.222222
3 35 97.222222
4 35 97.222222
5 35 97.222222
6 35 97.222222
7 0 0.000000
8 0 0.000000
9 0 0.000000
10 0 0.000000
11 0 0.000000
12 0 0.000000
13 0 0.000000
14 0 0.000000
15 0 0.000000
16 0 0.000000
17 0 0.000000
18 0 0.000000
19 0 0.000000
20 0 0.000000
21 0 0.000000
22 0 0.000000
23 0 0.000000
24 0 0.000000
25 0 0.000000
26 0 0.000000
27 0 0.000000
28 0 0.000000
29 0 0.000000
30 0 0.000000
31 0 0.000000
32 0 0.000000
33 0 0.000000
34 0 0.000000
35 0 0.000000
36 0 0.000000
37 0 0.000000
38 0 0.000000
39 0 0.000000
40 0 0.000000
41 0 0.000000
42 0 0.000000
43 0 0.000000
44 0 0.000000
45 0 0.000000
46 0 0.000000
47 0 0.000000
48 0 0.000000
49 0 0.000000
50 0 0.000000
51 0 0.000000
52 0 0.000000
53 0 0.000000
54 0 0.000000
55 0 0.000000
56 0 0.000000
57 0 0.000000
58 0 0.000000
59 0 0.000000
60 0 0.000000

From the statistical results, it is evident that all missing values are located in the first 6 rows.
Therefore, drop the first 6 rows of this dataset.

InΒ [20]:
# Delete lines 0 to 6
df_jobonline_all = df_jobonline_all.drop(index=range(7))  

df_jobonline_all.head()
Out[20]:
Date Auckland Bay of Plenty Canterbury Gisborne Hawke's Bay Marlborough/NelsonTasman/West Coast Manawatu/Wanganui-Taranaki Northland Otago Southland Waikato Wellington ids_Accounting ids_Construction ids_Education ids_Health ids_Hospitalty ids_IT ids_Manufacturing ids_Primary ids_Sales ids_Other ocp_Managers ocp_Professionals ocp_Tech & Trades ocp_Community & Personal Services ocp_Clerical & Administration ocp_Sales ocp_Machinery Drivers ocp_Labourers ocp_highly skilled ocp_skilled ocp_semi-skilled ocp_low skilled ocp_unskilled ocp_Skilled ocp_Unskilled
7 2010-12-01 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000
8 2011-03-01 101.457 101.561 107.036 103.535 102.370 100.780 103.643 106.457 102.388 101.849 101.830 107.252 96.481 98.957 104.711 101.374 103.521 108.968 100.481 102.091 101.755 102.361 110.413 107.814 101.682 104.106 102.947 109.816 102.438 101.859 113.548 105.121 102.011 104.132 104.480
9 2011-06-01 101.942 102.781 129.833 106.559 105.940 102.055 108.489 111.892 105.167 102.005 105.035 114.110 92.110 101.100 108.760 102.307 109.045 118.109 101.360 106.037 103.000 105.634 119.675 114.379 105.660 107.007 109.098 116.151 105.300 102.154 125.963 110.445 103.550 107.945 109.077
10 2011-09-01 101.542 104.443 139.557 109.635 114.017 104.820 113.102 119.014 107.132 100.886 108.501 118.782 91.165 105.883 108.103 102.536 115.264 129.535 102.664 112.256 104.741 108.902 125.667 117.779 110.105 106.261 117.874 120.426 107.653 104.015 132.978 113.728 104.835 110.780 112.078
11 2011-12-01 101.208 106.354 150.820 111.782 122.670 108.420 112.774 126.815 107.699 100.552 109.625 122.864 89.039 111.135 107.003 102.640 117.823 138.805 103.434 115.405 106.173 109.886 128.853 121.391 111.857 103.751 123.715 126.862 108.200 106.747 135.339 114.841 107.073 111.802 113.392
InΒ [21]:
import matplotlib.pyplot as plt

# Select columns starting with β€˜ocp_’ to plot
ocp_columns = [col for col in df_jobonline_all.columns if col.startswith('ocp_')]
ocp_data = df_jobonline_all[ocp_columns]

plt.figure(figsize=(20, 6))

# plotting the 1st graph
# Set the diagram to the first position in a row of three columns
plt.subplot(1, 3, 1)  
for column in ocp_columns:
    plt.plot(df_jobonline_all['Date'], df_jobonline_all[column], marker=None, linestyle='-', label=column)

# Add legend and remove bottom frame
plt.legend(frameon=False)

# Select columns starting with β€˜ids_’ to plot
ids_columns = [col for col in df_jobonline_all.columns if col.startswith('ids_')]
ids_data = df_jobonline_all[ids_columns]

# plotting the 2nd graph
plt.subplot(1, 3, 2)
for column in ids_columns:
    plt.plot(df_jobonline_all['Date'], df_jobonline_all[column], marker=None, linestyle='-', label=column)

plt.legend(frameon=False)

# Selecting variables to plot other than β€˜Date’ and the above two groups of variables
other_columns = [col for col in df_jobonline_all.columns if col != 'Date' and col not in ocp_columns and col not in ids_columns]
other_data = df_jobonline_all[other_columns]

# plotting the 3rd graph
plt.subplot(1, 3, 3)
for column in other_columns:
    plt.plot(df_jobonline_all['Date'], df_jobonline_all[column], marker=None, linestyle='-', label=column)

plt.legend(frameon=False)

plt.tight_layout()

plt.show()
No description has been provided for this image

Data clean result:

  • These graphs show that as time series data, the variables exhibit smooth curves, indicating the absence of outliers. In addition, there is no discontinuity in the curves, indicating that there are no missing values in the dataset. Therefore, data cleaning was completed for this dataset.

Insights from the charts:

  • Also, we have some findings from the graphs.The data from 2020 onwards show a significant decline, mainly due to the contraction of the labour market caused by COVID-19. In 2022, there is a rapid recovery in the labour market and a peak in recruitment demand. However, there is a decline in market demand from 2023 onwards, suggesting that this is a normal adjustment after a brief recovery. The market is still in a downward trend, showing a continuous decline in demand for jobs.

1.2.1.2 Regional Employment DataΒΆ

The dataset contains quarterly employment data from 1999 to 2022, providing insight into employment trends across New Zealand's different regions and industries.
In addition, the dataset contains information on job creation and destruction, allowing detailed analysis of the dynamics of employment over time.

InΒ [22]:
# import data
regionjob = pd.read_csv('../AAA/datasets/regionjob.csv')
regionjob.head()
Out[22]:
Date Type Auckland Waikato Bay of Plenty Gisborne Hawke's Bay Taranaki Manawatu-Wanganui Wellington Tasman, Nelson, Marlborough, West Coast Canterbury Otago
0 1999Q2 Job Creation 41890 5570 3430 520 2040 1560 3480 20430 1900 9430 3070
1 1999Q3 Job Creation 2710 340 370 35 120 90 170 1080 130 670 170
2 1999Q4 Job Creation 3040 500 260 30 150 120 220 1100 100 820 220
3 2000Q1 Job Creation 3340 610 420 45 210 210 240 1530 130 750 210
4 2000Q2 Job Creation 3700 760 400 25 210 180 260 1820 150 1140 380
InΒ [23]:
regionjob.describe()
Out[23]:
Auckland Waikato Bay of Plenty Gisborne Hawke's Bay Taranaki Manawatu-Wanganui Wellington Tasman, Nelson, Marlborough, West Coast Canterbury Otago
count 190.000000 190.000000 190.000000 190.000000 190.000000 190.000000 190.000000 190.000000 190.000000 190.000000 190.000000
mean 4237.473684 614.368421 453.263158 55.221053 214.131579 150.921053 250.315789 1511.000000 208.473684 1066.684211 327.736842
std 2954.425062 405.542776 277.856276 41.516537 148.068369 110.653753 245.363148 1433.490412 138.220118 697.969129 222.624162
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 3307.500000 462.500000 320.000000 35.000000 160.000000 110.000000 190.000000 1110.000000 160.000000 782.500000 250.000000
50% 3930.000000 560.000000 400.000000 50.000000 200.000000 140.000000 220.000000 1400.000000 190.000000 965.000000 300.000000
75% 4727.500000 690.000000 497.500000 65.000000 230.000000 170.000000 260.000000 1617.500000 237.500000 1200.000000 380.000000
max 41890.000000 5570.000000 3430.000000 520.000000 2040.000000 1560.000000 3480.000000 20430.000000 1900.000000 9430.000000 3070.000000

From the descriptive statistics analysis, it is evident that the minimum value of 0 for job creation is not justified.
In addition, the maximum value is significantly above the 75% percentile, indicating the presence of outliers in the data set.
Further exploration of the distribution is necessary to identify and analyse these outliers.

InΒ [24]:
import pandas as pd
import matplotlib.pyplot as plt

# Selection of variables from columns 3 to 14
selected_columns = regionjob.iloc[:, 2:14]

# Setting the graphic size
plt.figure(figsize=(15, 10))

# Iterate through each column and plot a bar graph
for i, column in enumerate(selected_columns.columns):
    plt.subplot(3, 4, i + 1)
    plt.bar(regionjob.index, selected_columns[column], color='blue', edgecolor='none')
    plt.xlabel('Year')
    plt.ylabel(column)
    plt.title(column)

plt.tight_layout()
plt.show()
No description has been provided for this image

From the above visualisation, it is clear that the outliers are mainly concentrated in the initial data point, that is the first row of the dataset.
In addition, in the β€˜Trend of Hawke's Bay’ graph, there is an outlier in the middle region with a value of about 600, which is higher than the average value of 214. Considering that this outlier is an indicator of job creation, the magnitude of its change may be related to the changes in the economic environment at that time. Therefore, we decided not to treat it as an outlier but to keep it in the data set.

Next, move on to dealing with the 0 values in the dataset.

InΒ [25]:
# Count the number and location of zeros for each variable
zero_counts = (regionjob == 0).sum()
zero_locations = {col: regionjob.index[regionjob[col] == 0].tolist() for col in regionjob.columns}


print("Number of zeros for each variable:")
print(zero_counts)

print("\nThe location of the zero value of each variable:")
for col, locations in zero_locations.items():
    print(f"{col}: {locations}")
Number of zeros for each variable:
Date                                       0
Type                                       0
Auckland                                   1
Waikato                                    1
Bay of Plenty                              1
Gisborne                                   1
Hawke's Bay                                1
Taranaki                                   1
Manawatu-Wanganui                          1
Wellington                                 1
Tasman, Nelson, Marlborough, West Coast    1
Canterbury                                 1
Otago                                      1
dtype: int64

The location of the zero value of each variable:
Date: []
Type: []
Auckland: [95]
Waikato: [95]
Bay of Plenty: [95]
Gisborne: [95]
Hawke's Bay: [95]
Taranaki: [95]
Manawatu-Wanganui: [95]
Wellington: [95]
Tasman, Nelson, Marlborough, West Coast: [95]
Canterbury: [95]
Otago: [95]
InΒ [26]:
#View data at index [95]
print(regionjob.iloc[95])
Date                                                1999Q2
Type                                       Job Destruction
Auckland                                                 0
Waikato                                                  0
Bay of Plenty                                            0
Gisborne                                                 0
Hawke's Bay                                              0
Taranaki                                                 0
Manawatu-Wanganui                                        0
Wellington                                               0
Tasman, Nelson, Marlborough, West Coast                  0
Canterbury                                               0
Otago                                                    0
Name: 95, dtype: object

From the results above, it is noticed that the outliers and zeros are concentrated in the time period 1999Q2.
Therefore, the data for that time point is required to be deleted.

InΒ [27]:
regionjob = regionjob[regionjob['Date'] != '1999Q2']
regionjob.head()
Out[27]:
Date Type Auckland Waikato Bay of Plenty Gisborne Hawke's Bay Taranaki Manawatu-Wanganui Wellington Tasman, Nelson, Marlborough, West Coast Canterbury Otago
1 1999Q3 Job Creation 2710 340 370 35 120 90 170 1080 130 670 170
2 1999Q4 Job Creation 3040 500 260 30 150 120 220 1100 100 820 220
3 2000Q1 Job Creation 3340 610 420 45 210 210 240 1530 130 750 210
4 2000Q2 Job Creation 3700 760 400 25 210 180 260 1820 150 1140 380
5 2000Q3 Job Creation 3340 430 320 40 180 120 250 1530 130 690 200

When we look at changes in the employment environment across regions, we want to analyse them on an annual basis. Therefore, it is necessary to convert quarterly data to annual data. Since the values in the table represent job creation and job destruction for the current period, the data for each quarter can be directly summed up when converting to annual data.

InΒ [28]:
regionjob['Year'] = regionjob['Date'].str.extract(r'(\d{4})').astype(int)

# Aggregate data based on year and type and calculate the sum for each area
regionjob_yearly = regionjob.groupby(['Year', 'Type']).sum().reset_index()

regionjob_yearly.drop(columns='Date', inplace=True, errors='ignore') 


regionjob_yearly.head()
Out[28]:
Year Type Auckland Waikato Bay of Plenty Gisborne Hawke's Bay Taranaki Manawatu-Wanganui Wellington Tasman, Nelson, Marlborough, West Coast Canterbury Otago
0 1999 Job Creation 5750 840 630 65 270 210 390 2180 230 1490 390
1 1999 Job Destruction 4510 720 430 60 275 145 300 2130 165 990 570
2 2000 Job Creation 13440 2210 1420 150 880 630 970 6050 540 3290 980
3 2000 Job Destruction 13200 1800 1130 145 750 540 930 5350 510 3040 1070
4 2001 Job Creation 15100 2190 1350 165 810 600 850 6820 590 4580 1000

After converting and summing the quarterly data to annual data, we also need to pivot the Type column to adjust its value into the index for classification purposes. This will allow different types of data (such as Job Creation and Job Destruction) to be clearly categorised and compared.

InΒ [29]:
regionjob_pivot = regionjob_yearly.pivot_table(values=['Auckland', 'Waikato', 'Bay of Plenty', 'Gisborne', 
                              "Hawke's Bay", 'Taranaki', 'Manawatu-Wanganui', 'Wellington', 
                              'Tasman, Nelson, Marlborough, West Coast', 'Canterbury', 'Otago', 
                               ], index=['Type', 'Year'])
regionjob_pivot.head()
Out[29]:
Auckland Bay of Plenty Canterbury Gisborne Hawke's Bay Manawatu-Wanganui Otago Taranaki Tasman, Nelson, Marlborough, West Coast Waikato Wellington
Type Year
Job Creation 1999 5750 630 1490 65 270 390 390 210 230 840 2180
2000 13440 1420 3290 150 880 970 980 630 540 2210 6050
2001 15100 1350 4580 165 810 850 1000 600 590 2190 6820
2002 14720 1680 2940 190 1170 850 840 620 620 2080 5810
2003 14590 1940 3270 150 820 1040 1140 710 710 2220 4830

Based on the different types, the curves for 'Job Creation' and 'Job Destruction' are plotted separately in order to see the final distribution of the data.

InΒ [30]:
# Select data for β€˜Job Creation’ and β€˜Job Destruction’
job_creation = regionjob_pivot.loc['Job Creation']
job_destruction = regionjob_pivot.loc['Job Destruction']

# Plot the time series of β€˜Job Creation’
plt.figure(figsize=(8, 3))
for column in job_creation.columns:
    plt.plot(job_creation.index, job_creation[column], label=column)
plt.title('Job Creation Over Years', fontsize=10)
plt.xticks(rotation=45,fontsize=9)  
plt.legend(loc='upper left', bbox_to_anchor=(1,1.03),fontsize=9) 

# Set the range of the x-axis to align with the minimum and maximum years of the data
plt.xlim(min(job_creation.index), max(job_creation.index))
plt.show()

# Plot time series of β€˜Job Destruction’
plt.figure(figsize=(8, 3))
for column in job_destruction.columns:
    plt.plot(job_destruction.index, job_destruction[column], label=column)
plt.title('Job Destruction Over Years', fontsize=10)
plt.xticks(rotation=45,fontsize=9)  
plt.legend(loc='upper left', bbox_to_anchor=(1,1.03),fontsize=9) 

# Set the range of the x-axis to align with the minimum and maximum years of the data
plt.xlim(min(job_destruction.index), max(job_destruction.index))
plt.show()
No description has been provided for this image
No description has been provided for this image

Data cleaning results:

  • There are no obvious breaks or outliers in the time-series data for each region, and the lines are smooth and continuous, which indicates that the data have been well handled in the pre-processing stage for possible missing values or outliers.

Insights from the charts:

  • Employment varies considerably across New Zealand's regions, with Auckland, Wellington and Canterbury having the most active labour markets.
  • Auckland stands out as an economic centre with volatile labour markets. This may reflect its large size and diverse economy.

1.2.1.3 Labour Force StatusΒΆ

Labour force status refers to the employment status of persons aged 15-64.
This indicator is crucial for measuring the efficiency of the use of human resources in the labour market.

InΒ [31]:
# import data
file_path = "../AAA/datasets/Labour Force Status for people aged 15 to 64 years.csv"

labour_force_status = pd.read_csv(file_path) 

labour_force_status.head()
Out[31]:
Year Not in Labour Force Working Age Population Labour Force Participation Rate Unemployment Rate Employment Rate Total Labour Force
0 1986 .. .. .. .. .. ..
1 1987 545.5 2205.3 75.3 4.2 72.1 1659.8
2 1988 557.1 2221.2 74.9 4.5 71.6 1664
3 1989 592.9 2229.4 73.4 6.4 68.7 1636.5
4 1990 616.8 2236.9 72.4 7.4 67.1 1620.2

Firstly, the β€˜year’ variable was checked to determine the time scale of the dataset and to verify the accuracy of the β€˜year’ variable.

InΒ [32]:
# Set graphic style to white background without grid
sns.set(style="white")

# Plot histogram using seaborn
bins = range(int(labour_force_status['Year'].min()), int(labour_force_status['Year'].max()) + 2)
plt.figure(figsize=(8, 4))
ax = sns.histplot(labour_force_status['Year'], bins=bins, kde=False, color='#34a4eb', edgecolor=None)

# Set up graph titles and axis labels
plt.title('Histogram of Year')
ax.set_xlabel('')  
plt.ylabel('Frequency',fontsize=9)

# Set up labels to be displayed every 5 years
tick_labels = range(int(labour_force_status['Year'].min()), int(labour_force_status['Year'].max()) + 1, 5)
plt.xticks(tick_labels,fontsize=9)  

# Reduce the thickness of the frame lines
for spine in ax.spines.values():
    spine.set_linewidth(0.5)

plt.show()
No description has been provided for this image

The visual inspection shows that the data for the 'Year' variable are within the expected range.
On this basis, descriptive statistical analysis is carried out on the remaining variables in the dataset.

InΒ [33]:
labour_force_status.describe()
Out[33]:
Year
count 39.000000
mean 2005.000000
std 11.401754
min 1986.000000
25% 1995.500000
50% 2005.000000
75% 2014.500000
max 2024.000000

Variable "year" counts 39 while all other variables count 38, indicating one missing value for each variable except "year". At first sight, it appears that the anomalous data are present in the row corresponding to the year 1986.

Therefore, the next step is to remove this anomalous row from the data set.

InΒ [34]:
labour_force_status = labour_force_status.iloc[1:]
labour_force_status.head()
Out[34]:
Year Not in Labour Force Working Age Population Labour Force Participation Rate Unemployment Rate Employment Rate Total Labour Force
1 1987 545.5 2205.3 75.3 4.2 72.1 1659.8
2 1988 557.1 2221.2 74.9 4.5 71.6 1664
3 1989 592.9 2229.4 73.4 6.4 68.7 1636.5
4 1990 616.8 2236.9 72.4 7.4 67.1 1620.2
5 1991 606.7 2259.3 73.1 8.7 66.7 1652.6

Finally, kernel density plots will be generated for each variable as a check on the integrity and basic characteristics of the data set.

InΒ [35]:
# Convert all columns except β€˜Year’ to numeric values
for col in labour_force_status.columns[1:]:  
    labour_force_status[col] = pd.to_numeric(labour_force_status[col], errors='coerce')

# Set canvas size and subgraph layout
fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(20, 9))
axes = axes.flatten()  

# Plot the KDEs for each variable
for i, column in enumerate(labour_force_status.columns):
    sns.kdeplot(data=labour_force_status, x=column, ax=axes[i], fill=True)
    axes[i].set_title(column)
    axes[i].set_xlabel('')  
    axes[i].set_ylabel('Density')  

plt.tight_layout()
plt.show()
No description has been provided for this image

Data clean result

  • Data continuity: All variables show a continuous distribution with no obvious breaks or unusual jumps.
  • Data normality: Most of the variables are approximately symmetric
  • Data range: 'Labour force participation rate', 'Unemployment rate' and 'Employment rate' are within the expected percentage range. The above indicates that the data are well prepared for further analysis.

Insights from the charts

  • Labour force participation rate: A concentration between 75% and 80% indicates a high level of labour market activity, suggesting that a large proportion of the working age population is participating in the labour market.
  • Unemployment rate: The most common value is around 5%, which is usually considered a relatively healthy state of the labour market.
  • Employment rate: Peaks at around 70%, reflecting a relatively stable employment environment.

1.2.1.4 Median wageΒΆ

InΒ [36]:
# import data
file_path = "../AAA/datasets/age median wage.csv"

age_median_wage = pd.read_csv(file_path) 

age_median_wage.head()
Out[36]:
Date 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65 Total All Ages
0 1999Q2 1610 5650 7530 8050 7930 7800 7920 7800 7380 6630 2390 7020
1 1999Q3 1600 5870 7860 8400 8240 8080 8210 8040 7590 6790 2510 7300
2 1999Q4 2140 6320 8300 8960 8900 8750 8790 8600 8140 7360 2930 7870
3 2000Q1 2210 5810 7640 8230 8170 8030 8090 7910 7480 6750 2700 7230
4 2000Q2 1810 5970 7880 8450 8300 8150 8260 8110 7650 6950 2710 7390

Firstly, the data set needs to be checked for null values.

InΒ [37]:
missing_values_count = age_median_wage.isna().sum()
print(missing_values_count)
Date              0
15-19             0
20-24             0
25-29             0
30-34             0
35-39             0
40-44             0
45-49             0
50-54             0
55-59             0
60-64             0
65                0
Total All Ages    0
dtype: int64

Next step, plot box plots for each variable to see data completeness.

InΒ [38]:
sns.set(style="whitegrid")
age_median_wage_long = pd.melt(age_median_wage, id_vars=['Date'], var_name='Age Group', value_name='Median Wage')

# Plot box plots, including wage data for all age groups
plt.figure(figsize=(10, 5))
ax = sns.boxplot(x='Age Group', y='Median Wage', data=age_median_wage_long)

plt.xticks(rotation=45)
plt.title('Median Wage Distribution by Age Group')
plt.xlabel('')
plt.ylabel('')
plt.show()
No description has been provided for this image

Data Wrangling result

  • Outlier identification: There are a few outliers in the 15-19 age group, but they do not deviate much from the overall distribution.
  • Data consistency: The data are relatively consistent across the age groups, with no obvious data breaks or non-logical distributions, indicating good overall data consistency.

Insight from chart

  • Age group 15-19: This group has significantly lower wages than other age groups, in line with the characteristics of first-time or part-time students.
  • Ages 15-19 to 35-39: Median wages increase, reflecting the general pattern of career progression with experience and skills.
  • Ages 30 to 54: median wages and interquartile range are relatively stable, reflecting a stable career with little change in wages.
  • Age 55 and over: Median wages decline, particularly in the 65+ age group, possibly reflecting gradual retirement and reductions in working hours.

For further analysis, it's necessary to convert the dataset into an annual format. This is done as follows:

  1. Group the data by year.
  2. Calculate the average of all quarters within each year.
InΒ [39]:
# Extraction year
age_median_wage['Year'] = age_median_wage['Date'].str[:4].astype(int)

# Ensure that all non-date columns are of numeric type
numeric_cols = age_median_wage.select_dtypes(include=[np.number]).columns.tolist()

if 'Year' in numeric_cols:
    numeric_cols.remove('Year')
    
# Group by year and calculate the average of the numerical columns
age_median_wage_year = age_median_wage.groupby('Year',as_index=True)[numeric_cols].mean()

age_median_wage_year = age_median_wage_year.reset_index()
age_median_wage_year.iloc[:, 1:] = age_median_wage_year.iloc[:, 1:].round(1)
age_median_wage_year.head()
Out[39]:
Year 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65 Total All Ages
0 1999 1783.3 5946.7 7896.7 8470.0 8356.7 8210.0 8306.7 8146.7 7703.3 6926.7 2610.0 7396.7
1 2000 2017.5 6042.5 7960.0 8575.0 8477.5 8317.5 8390.0 8230.0 7757.5 7035.0 2845.0 7512.5
2 2001 2190.0 6190.0 8165.0 8825.0 8755.0 8602.5 8650.0 8507.5 8032.5 7262.5 3282.5 7745.0
3 2002 2355.0 6370.0 8392.5 9107.5 9067.5 8947.5 8945.0 8837.5 8385.0 7567.5 3675.0 8002.5
4 2003 2505.0 6522.5 8620.0 9397.5 9372.5 9270.0 9275.0 9167.5 8727.5 7900.0 4090.0 8260.0

The median wage dataset has been successfully transformed into an annual format to facilitate further analysis. For the labour market dataset, the pre-processing steps have been completed for

  1. Online job
  2. Regional employment
  3. Labour force status
  4. Median wagee:

1.2.2 GDP datasetsΒΆ

1.2.2.1 GPD regionΒΆ

InΒ [40]:
# import data
file_path = "../AAA/datasets/GDP_region.csv"
GDP_region = pd.read_csv(file_path) 

GDP_region
Out[40]:
Year Northland Auckland Waikato Bay of Plenty Gisborne Hawke's Bay Taranaki Manawatu-Whanganui Wellington West Coast Canterbury Otago Southland Marlborough Tasman/Nelson Total North Island Total South Island New Zealand
0 2000 2655 37716 8582 5430 901 3386 3595 4742 16021 647 12215 4265 2355 935 1900 83028 22317 105345
1 2001 2954 38850 9712 5843 945 3643 4378 5065 16250 734 12849 4606 2746 1025 2035 87640 23995 111635
2 2002 3188 41840 10595 6238 986 3882 4479 5421 17424 773 13795 5030 2966 1135 2219 94054 25919 119973
3 2003 3082 45772 10181 6405 1008 4090 4344 5434 18184 744 14675 5347 2882 1160 2300 98500 27108 125607
4 2004 3316 49398 11038 6810 1020 4368 4327 5782 19021 818 15851 5732 2994 1276 2481 105079 29152 134231
5 2005 3659 53230 11666 7297 1073 4690 4356 6144 19957 862 17328 6198 2972 1351 2661 112073 31372 143444
6 2006 3969 55823 12516 7664 1087 4814 4518 6590 21190 1002 18272 6463 3163 1429 2653 118170 32982 151152
7 2007 4345 57384 13869 8399 1170 4946 5287 6695 21615 1075 19368 6807 3373 1584 2864 123709 35070 158779
8 2008 4619 61743 14708 9110 1211 4854 7649 7245 23220 1260 20901 7322 3873 1802 3047 134359 38204 172564
9 2009 4734 61098 15526 9039 1285 5061 7924 7165 23816 1388 21611 7577 4123 1851 3166 135648 39715 175363
10 2010 4543 63382 15258 9409 1324 5150 7665 7544 25113 1351 22229 7894 4168 1794 3296 139388 40731 180119
11 2011 4842 65759 16075 9947 1394 5447 7977 7888 25501 1427 23219 8171 4498 1813 3446 144830 42574 187404
12 2012 4922 68978 17102 10321 1432 5575 7896 7997 26724 1517 24316 8402 4599 1919 3503 150947 44256 195203
13 2013 4899 71351 16725 10490 1427 5730 7967 7999 27149 1449 25371 8526 4432 2001 3581 153739 45360 199099
14 2014 5363 75444 18690 11030 1474 6044 8508 8558 28348 1548 28198 9224 5075 2224 3873 163457 50143 213600
15 2015 5555 81076 18754 11291 1541 6176 8329 8681 29588 1479 29434 9427 4841 2338 3996 170990 51516 222506
16 2016 5994 88139 19413 12287 1638 6444 7221 8944 30654 1380 30338 10057 4693 2440 4216 180735 53124 233859
17 2017 6428 93624 20709 13820 1707 7011 7279 9358 32240 1450 31189 10745 5122 2604 4492 192176 55602 247778
18 2018 6970 99794 22583 15106 1844 7567 8129 10123 33610 1632 33555 11754 5609 2798 4835 205727 60184 265911
19 2019 7238 105433 24349 16322 1999 7886 8245 10744 34849 1685 35066 12484 5932 2966 5162 217065 63295 280360
20 2020 7712 110312 25993 17283 2056 8397 8483 11344 37561 1725 37099 13097 6093 3135 5487 229139 66638 295777
21 2021 7804 113290 26954 18012 2256 8878 8077 12115 37606 1784 37011 12860 6180 3197 5582 234991 66613 301604
22 2022 8684 124270 29378 19527 2318 9773 8911 13110 40777 1798 40380 14016 7144 3283 6038 256748 72659 329407
23 2023 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
InΒ [41]:
GDP_region.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 24 entries, 0 to 23
Data columns (total 19 columns):
 #   Column              Non-Null Count  Dtype 
---  ------              --------------  ----- 
 0   Year                24 non-null     int64 
 1   Northland           24 non-null     object
 2   Auckland            24 non-null     object
 3   Waikato             24 non-null     object
 4   Bay of Plenty       24 non-null     object
 5   Gisborne            24 non-null     object
 6   Hawke's Bay         24 non-null     object
 7   Taranaki            24 non-null     object
 8   Manawatu-Whanganui  24 non-null     object
 9   Wellington          24 non-null     object
 10  West Coast          24 non-null     object
 11  Canterbury          24 non-null     object
 12  Otago               24 non-null     object
 13  Southland           24 non-null     object
 14  Marlborough         24 non-null     object
 15  Tasman/Nelson       24 non-null     object
 16  Total North Island  24 non-null     object
 17  Total South Island  24 non-null     object
 18  New Zealand         24 non-null     object
dtypes: int64(1), object(18)
memory usage: 3.7+ KB

Based on the above information, it is known that this dataset contains a total of 24 data entries and that there are no null values. However, there is an anomalous character '...' in the data for the year 2023. In order to further process this anomalous data, we need to take the following steps:

  1. Replacement of the anomalous character '...' with a null value for subsequent data imputation
  2. Plot the trend
  3. Selection of the data imputation method based on the trend
InΒ [42]:
# Replace β€˜...’ for NaN
GDP_region.replace('..', pd.NA, inplace=True)

GDP_region = GDP_region.apply(pd.to_numeric, errors='coerce')
print(GDP_region.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 24 entries, 0 to 23
Data columns (total 19 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   Year                24 non-null     int64  
 1   Northland           23 non-null     float64
 2   Auckland            23 non-null     float64
 3   Waikato             23 non-null     float64
 4   Bay of Plenty       23 non-null     float64
 5   Gisborne            23 non-null     float64
 6   Hawke's Bay         23 non-null     float64
 7   Taranaki            23 non-null     float64
 8   Manawatu-Whanganui  23 non-null     float64
 9   Wellington          23 non-null     float64
 10  West Coast          23 non-null     float64
 11  Canterbury          23 non-null     float64
 12  Otago               23 non-null     float64
 13  Southland           23 non-null     float64
 14  Marlborough         23 non-null     float64
 15  Tasman/Nelson       23 non-null     float64
 16  Total North Island  23 non-null     float64
 17  Total South Island  23 non-null     float64
 18  New Zealand         23 non-null     float64
dtypes: float64(18), int64(1)
memory usage: 3.7 KB
None

Plot trends to select data imputation method

InΒ [43]:
GDP_region.set_index('Year', inplace=True)

plt.figure(figsize=(8, 4))
for column in GDP_region.columns:
    plt.plot(GDP_region.index, GDP_region[column], marker='o', label=column)

plt.title('GDP Trends by Region')
plt.grid(False)
plt.show()
No description has been provided for this image

As can be seen from the graph above, GDP is rising in every region, suggesting that we can use the growth rates of recent years to predict the values for 2023.

InΒ [44]:
# Calculate the growth rate for each region from 2021 to 2022
growth_rates = (GDP_region.loc[2022] - GDP_region.loc[2021]) / GDP_region.loc[2021]

# Projected data for 2023 based on data for 2022 and calculated growth rates
GDP_region.loc[2023] = GDP_region.loc[2022] * (1 + growth_rates)

print(GDP_region.loc[2023])
Northland               9663.231164
Auckland              136314.175126
Waikato                32019.992728
Bay of Plenty          21169.427548
Gisborne                2381.703901
Hawke's Bay            10758.225839
Taranaki                9831.115637
Manawatu-Whanganui     14186.718943
Wellington             44215.383955
West Coast              1812.109865
Canterbury             44055.669936
Otago                  15275.914152
Southland               8258.371521
Marlborough             3371.313419
Tasman/Nelson           6531.251164
Total North Island    280519.405016
Total South Island     79253.753487
New Zealand           359772.985932
Name: 2023, dtype: float64

Finally, the data format is adjusted to preview the data.

InΒ [45]:
GDP_region.reset_index(inplace=True)

# Data formatting adjustments
GDP_region.iloc[:, 1:] = GDP_region.iloc[:, 1:].round(1)

GDP_region
Out[45]:
Year Northland Auckland Waikato Bay of Plenty Gisborne Hawke's Bay Taranaki Manawatu-Whanganui Wellington West Coast Canterbury Otago Southland Marlborough Tasman/Nelson Total North Island Total South Island New Zealand
0 2000 2655.0 37716.0 8582.0 5430.0 901.0 3386.0 3595.0 4742.0 16021.0 647.0 12215.0 4265.0 2355.0 935.0 1900.0 83028.0 22317.0 105345.0
1 2001 2954.0 38850.0 9712.0 5843.0 945.0 3643.0 4378.0 5065.0 16250.0 734.0 12849.0 4606.0 2746.0 1025.0 2035.0 87640.0 23995.0 111635.0
2 2002 3188.0 41840.0 10595.0 6238.0 986.0 3882.0 4479.0 5421.0 17424.0 773.0 13795.0 5030.0 2966.0 1135.0 2219.0 94054.0 25919.0 119973.0
3 2003 3082.0 45772.0 10181.0 6405.0 1008.0 4090.0 4344.0 5434.0 18184.0 744.0 14675.0 5347.0 2882.0 1160.0 2300.0 98500.0 27108.0 125607.0
4 2004 3316.0 49398.0 11038.0 6810.0 1020.0 4368.0 4327.0 5782.0 19021.0 818.0 15851.0 5732.0 2994.0 1276.0 2481.0 105079.0 29152.0 134231.0
5 2005 3659.0 53230.0 11666.0 7297.0 1073.0 4690.0 4356.0 6144.0 19957.0 862.0 17328.0 6198.0 2972.0 1351.0 2661.0 112073.0 31372.0 143444.0
6 2006 3969.0 55823.0 12516.0 7664.0 1087.0 4814.0 4518.0 6590.0 21190.0 1002.0 18272.0 6463.0 3163.0 1429.0 2653.0 118170.0 32982.0 151152.0
7 2007 4345.0 57384.0 13869.0 8399.0 1170.0 4946.0 5287.0 6695.0 21615.0 1075.0 19368.0 6807.0 3373.0 1584.0 2864.0 123709.0 35070.0 158779.0
8 2008 4619.0 61743.0 14708.0 9110.0 1211.0 4854.0 7649.0 7245.0 23220.0 1260.0 20901.0 7322.0 3873.0 1802.0 3047.0 134359.0 38204.0 172564.0
9 2009 4734.0 61098.0 15526.0 9039.0 1285.0 5061.0 7924.0 7165.0 23816.0 1388.0 21611.0 7577.0 4123.0 1851.0 3166.0 135648.0 39715.0 175363.0
10 2010 4543.0 63382.0 15258.0 9409.0 1324.0 5150.0 7665.0 7544.0 25113.0 1351.0 22229.0 7894.0 4168.0 1794.0 3296.0 139388.0 40731.0 180119.0
11 2011 4842.0 65759.0 16075.0 9947.0 1394.0 5447.0 7977.0 7888.0 25501.0 1427.0 23219.0 8171.0 4498.0 1813.0 3446.0 144830.0 42574.0 187404.0
12 2012 4922.0 68978.0 17102.0 10321.0 1432.0 5575.0 7896.0 7997.0 26724.0 1517.0 24316.0 8402.0 4599.0 1919.0 3503.0 150947.0 44256.0 195203.0
13 2013 4899.0 71351.0 16725.0 10490.0 1427.0 5730.0 7967.0 7999.0 27149.0 1449.0 25371.0 8526.0 4432.0 2001.0 3581.0 153739.0 45360.0 199099.0
14 2014 5363.0 75444.0 18690.0 11030.0 1474.0 6044.0 8508.0 8558.0 28348.0 1548.0 28198.0 9224.0 5075.0 2224.0 3873.0 163457.0 50143.0 213600.0
15 2015 5555.0 81076.0 18754.0 11291.0 1541.0 6176.0 8329.0 8681.0 29588.0 1479.0 29434.0 9427.0 4841.0 2338.0 3996.0 170990.0 51516.0 222506.0
16 2016 5994.0 88139.0 19413.0 12287.0 1638.0 6444.0 7221.0 8944.0 30654.0 1380.0 30338.0 10057.0 4693.0 2440.0 4216.0 180735.0 53124.0 233859.0
17 2017 6428.0 93624.0 20709.0 13820.0 1707.0 7011.0 7279.0 9358.0 32240.0 1450.0 31189.0 10745.0 5122.0 2604.0 4492.0 192176.0 55602.0 247778.0
18 2018 6970.0 99794.0 22583.0 15106.0 1844.0 7567.0 8129.0 10123.0 33610.0 1632.0 33555.0 11754.0 5609.0 2798.0 4835.0 205727.0 60184.0 265911.0
19 2019 7238.0 105433.0 24349.0 16322.0 1999.0 7886.0 8245.0 10744.0 34849.0 1685.0 35066.0 12484.0 5932.0 2966.0 5162.0 217065.0 63295.0 280360.0
20 2020 7712.0 110312.0 25993.0 17283.0 2056.0 8397.0 8483.0 11344.0 37561.0 1725.0 37099.0 13097.0 6093.0 3135.0 5487.0 229139.0 66638.0 295777.0
21 2021 7804.0 113290.0 26954.0 18012.0 2256.0 8878.0 8077.0 12115.0 37606.0 1784.0 37011.0 12860.0 6180.0 3197.0 5582.0 234991.0 66613.0 301604.0
22 2022 8684.0 124270.0 29378.0 19527.0 2318.0 9773.0 8911.0 13110.0 40777.0 1798.0 40380.0 14016.0 7144.0 3283.0 6038.0 256748.0 72659.0 329407.0
23 2023 9663.2 136314.2 32020.0 21169.4 2381.7 10758.2 9831.1 14186.7 44215.4 1812.1 44055.7 15275.9 8258.4 3371.3 6531.3 280519.4 79253.8 359773.0

1.2.2.2 GPD industryΒΆ

This dataset consists of New Zealand GDP data by industry for past years.

InΒ [46]:
# import data
file_path = "../AAA/datasets/GDP_industry.csv"
GDP_industry = pd.read_csv(file_path) 

GDP_industry.head()
Out[46]:
Year Agriculture Forestry, Fishing, and Mining Forestry, Fishing, Mining, Electricity, Gas, Water and Waste Services Primary Manufacturing Other Manufacturing Manufacturing Electricity, Gas, Water, and Waste services Construction Wholesale Trade Retail Trade Accommodation and Food Services Accommodation Food and beverage services Transport, Postal and Warehousing Information Media, Telecommunications and Other Services Financial and Insurance Services Rental, Hiring and Real Estate Services Owner-Occupied Property Operation Professional, Scientific, and Technical Services Administrative and Support Services Public Administration and Safety Education and Training Health Care and Social Assistance Total All Industries
0 2000 5165 3138 6388 11156 6458 17614 3250 5179 6211 5086 2086.0 701 1386 5274 8460 4956 6355 8416 7594 2064 4313 4838 5344 105345
1 2001 7124 3549 6718 12070 6510 18580 3169 5139 6621 5346 2317.0 810 1506 5297 8924 5390 6711 8522 7596 2067 4505 5066 5711 111635
2 2002 8048 3648 6935 12513 7038 19551 3287 5484 7433 6130 2521.0 891 1630 5612 9539 5906 7187 8707 8426 2321 4794 5207 6171 119973
3 2003 5750 3639 7337 13618 7405 21023 3698 6053 7259 6826 2477.0 851 1626 6129 10420 6430 7856 9221 9095 2553 5039 5553 6586 125607
4 2004 6251 3352 7562 13378 7890 21268 4210 6806 7932 7202 2832.0 978 1854 6586 11070 7289 8641 9840 9672 2733 5310 6044 7195 134231
InΒ [47]:
GDP_industry.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 23 entries, 0 to 22
Data columns (total 25 columns):
 #   Column                                                                 Non-Null Count  Dtype  
---  ------                                                                 --------------  -----  
 0   Year                                                                   23 non-null     int64  
 1   Agriculture                                                            23 non-null     int64  
 2   Forestry, Fishing, and Mining                                          23 non-null     int64  
 3   Forestry, Fishing, Mining, Electricity, Gas, Water and Waste Services  23 non-null     int64  
 4   Primary Manufacturing                                                  23 non-null     int64  
 5   Other Manufacturing                                                    23 non-null     int64  
 6   Manufacturing                                                          23 non-null     int64  
 7   Electricity, Gas, Water, and Waste services                            23 non-null     int64  
 8   Construction                                                           23 non-null     int64  
 9   Wholesale Trade                                                        23 non-null     int64  
 10  Retail Trade                                                           23 non-null     int64  
 11  Accommodation and Food Services                                        16 non-null     float64
 12  Accommodation                                                          23 non-null     int64  
 13  Food and beverage services                                             23 non-null     int64  
 14  Transport, Postal and Warehousing                                      23 non-null     int64  
 15  Information Media, Telecommunications and Other Services               23 non-null     int64  
 16  Financial and Insurance Services                                       23 non-null     int64  
 17  Rental, Hiring and Real Estate Services                                23 non-null     int64  
 18  Owner-Occupied Property Operation                                      23 non-null     int64  
 19  Professional, Scientific, and Technical Services                       23 non-null     int64  
 20  Administrative and Support Services                                    23 non-null     int64  
 21  Public Administration and Safety                                       23 non-null     int64  
 22  Education and Training                                                 23 non-null     int64  
 23  Health Care and Social Assistance                                      23 non-null     int64  
 24  Total All Industries                                                   23 non-null     int64  
dtypes: float64(1), int64(24)
memory usage: 4.6 KB

Examining the data shows that there are null values in the variable 'Accommodation and Food Services' and further examination of all values in this variable is required.

InΒ [48]:
print(GDP_industry['Accommodation and Food Services'])
0     2086.0
1     2317.0
2     2521.0
3     2477.0
4     2832.0
5     3148.0
6     3216.0
7     3365.0
8     3649.0
9     3505.0
10    3789.0
11    3857.0
12    3940.0
13    4124.0
14    4368.0
15    4763.0
16       NaN
17       NaN
18       NaN
19       NaN
20       NaN
21       NaN
22       NaN
Name: Accommodation and Food Services, dtype: float64

To complete imputing this variable the following strategy is used

  1. Identify the variable most correlated with 'accommodation and food services'.
  2. Use the data from the variable with the highest correlation to run a regression.
  3. Apply regression analysis to forecast the GDP of the 'Accommodation and Food Services' industry
InΒ [49]:
# Calculate the correlation coefficient matrix
corr_matrix = GDP_industry.drop('Year', axis=1).corr()

# Plot heat map
plt.figure(figsize=(8, 6))  
sns.heatmap(corr_matrix, annot=False, cmap='coolwarm', cbar=True)
plt.show()
No description has been provided for this image

From the heat map it can be seen that the variable with the highest correlation with the variable 'accommodation and food services' is 'food and beverage services'. Therefore, 'Food and Beverage Services' is used for the regression prediction.

InΒ [50]:
from sklearn.linear_model import LinearRegression

# Split data into parts with and without missing values
train_data = GDP_industry.dropna(subset=['Accommodation and Food Services'])
test_data = GDP_industry[GDP_industry['Accommodation and Food Services'].isna()]

# Define the independent and dependent variables
X_train = train_data[['Food and beverage services']]
y_train = train_data['Accommodation and Food Services']
X_test = test_data[['Food and beverage services']]

# Build a linear regression model
model = LinearRegression()
model.fit(X_train, y_train)

# Predicted missing values
predicted_values = model.predict(X_test)

# Impute missing value
GDP_industry.loc[GDP_industry['Accommodation and Food Services'].isna(), 'Accommodation and Food Services'] = predicted_values

# Display of final data
GDP_industry['Accommodation and Food Services'] = GDP_industry['Accommodation and Food Services'].astype('int64')
GDP_industry['Accommodation and Food Services']
Out[50]:
0     2086
1     2317
2     2521
3     2477
4     2832
5     3148
6     3216
7     3365
8     3649
9     3505
10    3789
11    3857
12    3940
13    4124
14    4368
15    4763
16    5327
17    5947
18    6247
19    6471
20    6823
21    6002
22    6851
Name: Accommodation and Food Services, dtype: int64

For the GDP dataset, the pre-processing steps have been completed for

  • GDP by region
  • GDP by industry

2.Data IntegrationΒΆ

For further analysis, we need to merge the static datasets from the data pre-processing stage into one master table. Including:
1.Job market

  • Online job
  • Regional employment
  • Labour force status
  • Median wagee:

2.GDP

  • GDP by region
  • GDP by industry

To complete the data integration, the following steps were taken:

  • Preparation: The structure and naming of each table are standardised to ensure consistency across data sources.
  • Merge: A custom function was implemented to merge any number of datasets with a common key efficiently. This allows us to input all datasets simultaneously and merge them quickly.
  • Big data processing optimisation: Using Dask DataFrame and HDF5 formats. This phase also included creating a custom function to quickly rename columns.

2.1 Job market datasetsΒΆ

2.1.1 Online job datasetsΒΆ

The dataset is collected quarterly and must be converted to annual data for merging.

InΒ [51]:
# Convert a date string to a date object
df_jobonline_all['Date'] = pd.to_datetime(df_jobonline_all['Date'])

# Calculate quarters and format dates
df_jobonline_all['Date'] = df_jobonline_all['Date'].dt.year.astype(str) + 'Q' + ((df_jobonline_all['Date'].dt.month - 1) // 3 + 1).astype(str)

df_jobonline_all['Year'] = df_jobonline_all['Date'].str[:4].astype(int)
numeric_cols = df_jobonline_all.select_dtypes(include=[np.number]).columns.tolist()

if 'Year' in numeric_cols:
    numeric_cols.remove('Year')
    
# Grouping by year and averaging the numerical columns
job_online = df_jobonline_all.groupby('Year',as_index=True)[numeric_cols].mean()

job_online.reset_index(drop=False, inplace=True)
job_online.head()
Out[51]:
Year Auckland Bay of Plenty Canterbury Gisborne Hawke's Bay Marlborough/NelsonTasman/West Coast Manawatu/Wanganui-Taranaki Northland Otago Southland Waikato Wellington ids_Accounting ids_Construction ids_Education ids_Health ids_Hospitalty ids_IT ids_Manufacturing ids_Primary ids_Sales ids_Other ocp_Managers ocp_Professionals ocp_Tech & Trades ocp_Community & Personal Services ocp_Clerical & Administration ocp_Sales ocp_Machinery Drivers ocp_Labourers ocp_highly skilled ocp_skilled ocp_semi-skilled ocp_low skilled ocp_unskilled ocp_Skilled ocp_Unskilled
0 2010 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.0000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.0000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000
1 2011 101.53725 103.78475 131.81150 107.87775 111.24925 104.01875 109.5020 116.04450 105.59650 101.32300 106.24775 115.75200 92.19875 104.26875 107.14425 102.21425 111.41325 123.85425 101.98475 108.94725 103.91725 106.69575 121.15200 115.34075 107.32600 105.28125 113.4085 118.31375 105.89775 103.69375 126.95700 111.03375 104.36725 108.66475 109.75675
2 2012 104.07175 110.63300 170.32750 120.65275 129.16900 117.80575 115.1725 138.57875 110.88150 102.74725 109.49900 147.46800 83.28000 119.27800 112.45725 97.90175 125.17300 148.83975 105.11225 113.70325 106.71575 107.81950 142.38200 133.33875 113.67750 107.55275 137.1715 145.01325 107.70150 110.10950 150.74725 120.93250 116.90500 113.88775 120.46675
3 2013 116.27650 130.56400 191.61625 127.15625 136.18075 137.26350 120.3040 150.94075 123.46825 106.19475 112.27775 184.80025 92.26350 123.59450 134.52225 92.37850 153.48125 158.91900 113.93700 122.01075 116.59175 108.56500 171.24675 157.54900 120.54600 118.16900 180.6850 190.80675 110.85300 123.29000 181.36625 137.46225 138.64800 123.33150 138.68850
4 2014 133.69175 154.13400 220.14950 143.16750 165.08475 144.57075 141.1915 188.93475 140.00225 111.70625 121.78200 230.17750 106.67700 129.58350 162.81525 95.91100 181.68300 183.42750 128.40525 121.79475 135.62400 115.74175 207.52175 180.00800 132.23875 131.47725 223.2990 249.28800 120.81950 142.89900 209.12425 158.86300 167.51250 138.88250 162.42250

Change column names to make it easier to identify variables in the merged table.

InΒ [52]:
# Get all the columns except β€˜Year’
columns_to_rename = [col for col in job_online.columns if col != 'Year']

# Construct a dictionary that maps old column names to new column names
rename_dict = {col: 'AVI_' + col for col in columns_to_rename}

# Use the rename() method to rename a column
job_online =job_online.rename(columns=rename_dict)

job_online.head()
Out[52]:
Year AVI_Auckland AVI_Bay of Plenty AVI_Canterbury AVI_Gisborne Hawke's Bay AVI_Marlborough/NelsonTasman/West Coast AVI_Manawatu/Wanganui-Taranaki AVI_Northland AVI_Otago Southland AVI_Waikato AVI_Wellington AVI_ids_Accounting AVI_ids_Construction AVI_ids_Education AVI_ids_Health AVI_ids_Hospitalty AVI_ids_IT AVI_ids_Manufacturing AVI_ids_Primary AVI_ids_Sales AVI_ids_Other AVI_ocp_Managers AVI_ocp_Professionals AVI_ocp_Tech & Trades AVI_ocp_Community & Personal Services AVI_ocp_Clerical & Administration AVI_ocp_Sales AVI_ocp_Machinery Drivers AVI_ocp_Labourers AVI_ocp_highly skilled AVI_ocp_skilled AVI_ocp_semi-skilled AVI_ocp_low skilled AVI_ocp_unskilled AVI_ocp_Skilled AVI_ocp_Unskilled
0 2010 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.0000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.0000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000
1 2011 101.53725 103.78475 131.81150 107.87775 111.24925 104.01875 109.5020 116.04450 105.59650 101.32300 106.24775 115.75200 92.19875 104.26875 107.14425 102.21425 111.41325 123.85425 101.98475 108.94725 103.91725 106.69575 121.15200 115.34075 107.32600 105.28125 113.4085 118.31375 105.89775 103.69375 126.95700 111.03375 104.36725 108.66475 109.75675
2 2012 104.07175 110.63300 170.32750 120.65275 129.16900 117.80575 115.1725 138.57875 110.88150 102.74725 109.49900 147.46800 83.28000 119.27800 112.45725 97.90175 125.17300 148.83975 105.11225 113.70325 106.71575 107.81950 142.38200 133.33875 113.67750 107.55275 137.1715 145.01325 107.70150 110.10950 150.74725 120.93250 116.90500 113.88775 120.46675
3 2013 116.27650 130.56400 191.61625 127.15625 136.18075 137.26350 120.3040 150.94075 123.46825 106.19475 112.27775 184.80025 92.26350 123.59450 134.52225 92.37850 153.48125 158.91900 113.93700 122.01075 116.59175 108.56500 171.24675 157.54900 120.54600 118.16900 180.6850 190.80675 110.85300 123.29000 181.36625 137.46225 138.64800 123.33150 138.68850
4 2014 133.69175 154.13400 220.14950 143.16750 165.08475 144.57075 141.1915 188.93475 140.00225 111.70625 121.78200 230.17750 106.67700 129.58350 162.81525 95.91100 181.68300 183.42750 128.40525 121.79475 135.62400 115.74175 207.52175 180.00800 132.23875 131.47725 223.2990 249.28800 120.81950 142.89900 209.12425 158.86300 167.51250 138.88250 162.42250

2.1.2 Region dataΒΆ

InΒ [53]:
# Add prefixes and rename variables

job_creation.reset_index(drop=False, inplace=True)
# Get all the columns except β€˜Year’
columns_to_rename = [col for col in job_creation.columns if col != 'Year']

# Construct a dictionary that maps old column names to new column names
rename_dict = {col: 'job_creation_' + col for col in columns_to_rename}

job_creation = job_creation.rename(columns=rename_dict)

job_creation.head()
Out[53]:
Year job_creation_Auckland job_creation_Bay of Plenty job_creation_Canterbury job_creation_Gisborne job_creation_Hawke's Bay job_creation_Manawatu-Wanganui job_creation_Otago job_creation_Taranaki job_creation_Tasman, Nelson, Marlborough, West Coast job_creation_Waikato job_creation_Wellington
0 1999 5750 630 1490 65 270 390 390 210 230 840 2180
1 2000 13440 1420 3290 150 880 970 980 630 540 2210 6050
2 2001 15100 1350 4580 165 810 850 1000 600 590 2190 6820
3 2002 14720 1680 2940 190 1170 850 840 620 620 2080 5810
4 2003 14590 1940 3270 150 820 1040 1140 710 710 2220 4830
InΒ [54]:
# Add prefixes and rename variables

job_destruction.reset_index(drop=False, inplace=True)
columns_to_rename = [col for col in job_destruction.columns if col != 'Year']

rename_dict = {col: 'job_destruction_' + col for col in columns_to_rename}

job_destruction = job_destruction.rename(columns=rename_dict)

job_destruction.head()
Out[54]:
Year job_destruction_Auckland job_destruction_Bay of Plenty job_destruction_Canterbury job_destruction_Gisborne job_destruction_Hawke's Bay job_destruction_Manawatu-Wanganui job_destruction_Otago job_destruction_Taranaki job_destruction_Tasman, Nelson, Marlborough, West Coast job_destruction_Waikato job_destruction_Wellington
0 1999 4510 430 990 60 275 300 570 145 165 720 2130
1 2000 13200 1130 3040 145 750 930 1070 540 510 1800 5350
2 2001 12410 940 4010 120 730 980 870 450 510 1460 6180
3 2002 12760 1660 2960 165 890 710 790 480 500 1640 5970
4 2003 12310 1680 2480 145 880 770 740 430 530 1420 5010
InΒ [55]:
job_creation['Year'] = job_creation['Year'].astype(int)
job_destruction['Year'] = job_destruction['Year'].astype(int)

2.1.3 Median WageΒΆ

InΒ [56]:
# Add prefixes and rename variables
columns_to_rename = [col for col in age_median_wage_year.columns if col != 'Year']

rename_dict = {col: 'median_wage_' + col for col in columns_to_rename}
age_median_wage_year = age_median_wage_year.rename(columns=rename_dict)

age_median_wage_year.head()
Out[56]:
Year median_wage_15-19 median_wage_20-24 median_wage_25-29 median_wage_30-34 median_wage_35-39 median_wage_40-44 median_wage_45-49 median_wage_50-54 median_wage_55-59 median_wage_60-64 median_wage_65 median_wage_Total All Ages
0 1999 1783.3 5946.7 7896.7 8470.0 8356.7 8210.0 8306.7 8146.7 7703.3 6926.7 2610.0 7396.7
1 2000 2017.5 6042.5 7960.0 8575.0 8477.5 8317.5 8390.0 8230.0 7757.5 7035.0 2845.0 7512.5
2 2001 2190.0 6190.0 8165.0 8825.0 8755.0 8602.5 8650.0 8507.5 8032.5 7262.5 3282.5 7745.0
3 2002 2355.0 6370.0 8392.5 9107.5 9067.5 8947.5 8945.0 8837.5 8385.0 7567.5 3675.0 8002.5
4 2003 2505.0 6522.5 8620.0 9397.5 9372.5 9270.0 9275.0 9167.5 8727.5 7900.0 4090.0 8260.0

2.2 GDP datasetsΒΆ

2.2.1 GDP regionΒΆ

InΒ [57]:
# Add prefixes and rename variables
columns_to_rename = [col for col in GDP_region.columns if col != 'Year']

rename_dict = {col: 'GDP_' + col for col in columns_to_rename}
GDP_region = GDP_region.rename(columns=rename_dict)

GDP_region.head()
Out[57]:
Year GDP_Northland GDP_Auckland GDP_Waikato GDP_Bay of Plenty GDP_Gisborne GDP_Hawke's Bay GDP_Taranaki GDP_Manawatu-Whanganui GDP_Wellington GDP_West Coast GDP_Canterbury GDP_Otago GDP_Southland GDP_Marlborough GDP_Tasman/Nelson GDP_Total North Island GDP_Total South Island GDP_New Zealand
0 2000 2655.0 37716.0 8582.0 5430.0 901.0 3386.0 3595.0 4742.0 16021.0 647.0 12215.0 4265.0 2355.0 935.0 1900.0 83028.0 22317.0 105345.0
1 2001 2954.0 38850.0 9712.0 5843.0 945.0 3643.0 4378.0 5065.0 16250.0 734.0 12849.0 4606.0 2746.0 1025.0 2035.0 87640.0 23995.0 111635.0
2 2002 3188.0 41840.0 10595.0 6238.0 986.0 3882.0 4479.0 5421.0 17424.0 773.0 13795.0 5030.0 2966.0 1135.0 2219.0 94054.0 25919.0 119973.0
3 2003 3082.0 45772.0 10181.0 6405.0 1008.0 4090.0 4344.0 5434.0 18184.0 744.0 14675.0 5347.0 2882.0 1160.0 2300.0 98500.0 27108.0 125607.0
4 2004 3316.0 49398.0 11038.0 6810.0 1020.0 4368.0 4327.0 5782.0 19021.0 818.0 15851.0 5732.0 2994.0 1276.0 2481.0 105079.0 29152.0 134231.0

2.2.2 GDP industryΒΆ

InΒ [58]:
# Add prefixes and rename variables
columns_to_rename = [col for col in GDP_industry.columns if col != 'Year']

rename_dict = {col: 'GDP_' + col for col in columns_to_rename}

GDP_industry = GDP_industry.rename(columns=rename_dict)

GDP_industry.head()
Out[58]:
Year GDP_Agriculture GDP_Forestry, Fishing, and Mining GDP_Forestry, Fishing, Mining, Electricity, Gas, Water and Waste Services GDP_Primary Manufacturing GDP_Other Manufacturing GDP_Manufacturing GDP_Electricity, Gas, Water, and Waste services GDP_Construction GDP_Wholesale Trade GDP_Retail Trade GDP_Accommodation and Food Services GDP_Accommodation GDP_Food and beverage services GDP_Transport, Postal and Warehousing GDP_Information Media, Telecommunications and Other Services GDP_Financial and Insurance Services GDP_Rental, Hiring and Real Estate Services GDP_Owner-Occupied Property Operation GDP_Professional, Scientific, and Technical Services GDP_Administrative and Support Services GDP_Public Administration and Safety GDP_Education and Training GDP_Health Care and Social Assistance GDP_Total All Industries
0 2000 5165 3138 6388 11156 6458 17614 3250 5179 6211 5086 2086 701 1386 5274 8460 4956 6355 8416 7594 2064 4313 4838 5344 105345
1 2001 7124 3549 6718 12070 6510 18580 3169 5139 6621 5346 2317 810 1506 5297 8924 5390 6711 8522 7596 2067 4505 5066 5711 111635
2 2002 8048 3648 6935 12513 7038 19551 3287 5484 7433 6130 2521 891 1630 5612 9539 5906 7187 8707 8426 2321 4794 5207 6171 119973
3 2003 5750 3639 7337 13618 7405 21023 3698 6053 7259 6826 2477 851 1626 6129 10420 6430 7856 9221 9095 2553 5039 5553 6586 125607
4 2004 6251 3352 7562 13378 7890 21268 4210 6806 7932 7202 2832 978 1854 6586 11070 7289 8641 9840 9672 2733 5310 6044 7195 134231
InΒ [Β ]:
 

2.3 Merge datasetsΒΆ

Define a function to merge different datasets.
The merge_datasets function is designed to merge multiple datasets based on a common key, in this case the 'Year' column. It takes any number of datasets as input and merges them iteratively using an inner join operation on the 'Year' column. It returns the merged dataset. This function provides a convenient way of efficiently merging datasets with a common key.

InΒ [59]:
def merge_datasets(*datasets):

    merged_dataset = datasets[0]  # Initialize with the first dataset
    
    # Iterate over the remaining datasets and merge them one by one
    for dataset in datasets[1:]:
        merged_dataset = pd.merge(merged_dataset, dataset, on='Year', how='inner')
    
    return merged_dataset

Merge all processed datasets at once using a custom function.

InΒ [60]:
merged_data = merge_datasets(job_creation, job_destruction, labour_force_status, job_online,age_median_wage_year,GDP_region, GDP_industry)
merged_data
Out[60]:
Year job_creation_Auckland job_creation_Bay of Plenty job_creation_Canterbury job_creation_Gisborne job_creation_Hawke's Bay job_creation_Manawatu-Wanganui job_creation_Otago job_creation_Taranaki job_creation_Tasman, Nelson, Marlborough, West Coast job_creation_Waikato job_creation_Wellington job_destruction_Auckland job_destruction_Bay of Plenty job_destruction_Canterbury job_destruction_Gisborne job_destruction_Hawke's Bay job_destruction_Manawatu-Wanganui job_destruction_Otago job_destruction_Taranaki job_destruction_Tasman, Nelson, Marlborough, West Coast job_destruction_Waikato job_destruction_Wellington Not in Labour Force Working Age Population Labour Force Participation Rate Unemployment Rate Employment Rate Total Labour Force AVI_Auckland AVI_Bay of Plenty AVI_Canterbury AVI_Gisborne Hawke's Bay AVI_Marlborough/NelsonTasman/West Coast AVI_Manawatu/Wanganui-Taranaki AVI_Northland AVI_Otago Southland AVI_Waikato AVI_Wellington AVI_ids_Accounting AVI_ids_Construction AVI_ids_Education AVI_ids_Health AVI_ids_Hospitalty AVI_ids_IT AVI_ids_Manufacturing AVI_ids_Primary AVI_ids_Sales AVI_ids_Other AVI_ocp_Managers AVI_ocp_Professionals AVI_ocp_Tech & Trades AVI_ocp_Community & Personal Services AVI_ocp_Clerical & Administration AVI_ocp_Sales AVI_ocp_Machinery Drivers AVI_ocp_Labourers AVI_ocp_highly skilled AVI_ocp_skilled AVI_ocp_semi-skilled AVI_ocp_low skilled AVI_ocp_unskilled AVI_ocp_Skilled AVI_ocp_Unskilled median_wage_15-19 median_wage_20-24 median_wage_25-29 median_wage_30-34 median_wage_35-39 median_wage_40-44 median_wage_45-49 median_wage_50-54 median_wage_55-59 median_wage_60-64 median_wage_65 median_wage_Total All Ages GDP_Northland GDP_Auckland GDP_Waikato GDP_Bay of Plenty GDP_Gisborne GDP_Hawke's Bay GDP_Taranaki GDP_Manawatu-Whanganui GDP_Wellington GDP_West Coast GDP_Canterbury GDP_Otago GDP_Southland GDP_Marlborough GDP_Tasman/Nelson GDP_Total North Island GDP_Total South Island GDP_New Zealand GDP_Agriculture GDP_Forestry, Fishing, and Mining GDP_Forestry, Fishing, Mining, Electricity, Gas, Water and Waste Services GDP_Primary Manufacturing GDP_Other Manufacturing GDP_Manufacturing GDP_Electricity, Gas, Water, and Waste services GDP_Construction GDP_Wholesale Trade GDP_Retail Trade GDP_Accommodation and Food Services GDP_Accommodation GDP_Food and beverage services GDP_Transport, Postal and Warehousing GDP_Information Media, Telecommunications and Other Services GDP_Financial and Insurance Services GDP_Rental, Hiring and Real Estate Services GDP_Owner-Occupied Property Operation GDP_Professional, Scientific, and Technical Services GDP_Administrative and Support Services GDP_Public Administration and Safety GDP_Education and Training GDP_Health Care and Social Assistance GDP_Total All Industries
0 2010 18130 1580 3320 250 830 840 1240 475 800 2730 4910 15380 1430 3470 195 850 980 1300 515 900 2270 5290 645.7 2844.4 77.3 6.3 72.4 2198.7 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 3060.0 8050.0 10582.5 11865.0 12315.0 12137.5 12065.0 11905.0 11437.5 10515.0 7245.0 10632.5 4543.0 63382.0 15258.0 9409.0 1324.0 5150.0 7665.0 7544.0 25113.0 1351.0 22229.0 7894.0 4168.0 1794.0 3296.0 139388.0 40731.0 180119.0 8654 6651 12720 13001 7925 20926 6069 10464 9869 8292 3789 1226 2563 7929 12490 11119 13413 12192 14735 3633 8732 9204 11958 180119
1 2011 17310 1600 4100 190 710 810 1140 480 850 2220 5310 14540 1370 3730 190 690 890 1110 490 760 1920 5120 655.8 2866.7 77.1 6.4 72.2 2211.0 101.53725 103.78475 131.81150 107.87775 111.24925 104.01875 109.50200 116.04450 105.59650 101.32300 106.24775 115.75200 92.19875 104.26875 107.14425 102.21425 111.41325 123.85425 101.98475 108.94725 103.91725 106.69575 121.15200 115.34075 107.32600 105.28125 113.40850 118.31375 105.89775 103.69375 126.95700 111.03375 104.36725 108.66475 109.75675 3152.5 8162.5 10835.0 12155.0 12712.5 12565.0 12462.5 12290.0 11845.0 10967.5 7752.5 10972.5 4842.0 65759.0 16075.0 9947.0 1394.0 5447.0 7977.0 7888.0 25501.0 1427.0 23219.0 8171.0 4498.0 1813.0 3446.0 144830.0 42574.0 187404.0 10643 6938 13291 13860 8191 22051 6353 10470 10081 8469 3857 1248 2609 8758 12421 9975 13648 13484 15529 3959 8835 9595 12337 187404
2 2012 17260 1560 4580 195 820 1040 1230 510 800 2710 5880 14410 1510 3710 160 920 940 1200 450 790 2290 5080 652.5 2876.3 77.3 6.3 72.4 2223.8 104.07175 110.63300 170.32750 120.65275 129.16900 117.80575 115.17250 138.57875 110.88150 102.74725 109.49900 147.46800 83.28000 119.27800 112.45725 97.90175 125.17300 148.83975 105.11225 113.70325 106.71575 107.81950 142.38200 133.33875 113.67750 107.55275 137.17150 145.01325 107.70150 110.10950 150.74725 120.93250 116.90500 113.88775 120.46675 3257.5 8370.0 11112.5 12452.5 13102.5 13020.0 12925.0 12735.0 12280.0 11435.0 8245.0 11335.0 4922.0 68978.0 17102.0 10321.0 1432.0 5575.0 7896.0 7997.0 26724.0 1517.0 24316.0 8402.0 4599.0 1919.0 3503.0 150947.0 44256.0 195203.0 10622 6772 13371 15298 8017 23315 6600 10804 10526 8878 3940 1245 2696 9331 12960 10625 14757 14344 15869 4172 9041 9985 12665 195203
3 2013 18320 1800 4360 305 790 770 1290 660 830 2110 5820 13340 1470 3770 245 710 780 1070 480 760 1950 4720 666.9 2878.8 76.8 6.6 71.8 2211.9 116.27650 130.56400 191.61625 127.15625 136.18075 137.26350 120.30400 150.94075 123.46825 106.19475 112.27775 184.80025 92.26350 123.59450 134.52225 92.37850 153.48125 158.91900 113.93700 122.01075 116.59175 108.56500 171.24675 157.54900 120.54600 118.16900 180.68500 190.80675 110.85300 123.29000 181.36625 137.46225 138.64800 123.33150 138.68850 3290.0 8555.0 11340.0 12705.0 13407.5 13420.0 13267.5 13065.0 12647.5 11850.0 8620.0 11617.5 4899.0 71351.0 16725.0 10490.0 1427.0 5730.0 7967.0 7999.0 27149.0 1449.0 25371.0 8526.0 4432.0 2001.0 3581.0 153739.0 45360.0 199099.0 9130 6610 13764 15133 7938 23071 7154 11541 10530 9489 4124 1270 2855 9790 13188 10921 15134 15260 16555 4165 9147 10154 13133 199099
4 2014 18870 1580 4710 285 690 860 1380 550 1010 2350 6050 15940 1620 3600 245 720 800 1180 600 690 2400 4680 641.8 2899.4 77.9 6.0 73.2 2257.6 133.69175 154.13400 220.14950 143.16750 165.08475 144.57075 141.19150 188.93475 140.00225 111.70625 121.78200 230.17750 106.67700 129.58350 162.81525 95.91100 181.68300 183.42750 128.40525 121.79475 135.62400 115.74175 207.52175 180.00800 132.23875 131.47725 223.29900 249.28800 120.81950 142.89900 209.12425 158.86300 167.51250 138.88250 162.42250 3392.5 8820.0 11590.0 13025.0 13767.5 13877.5 13655.0 13475.0 13017.5 12245.0 9032.5 11925.0 5363.0 75444.0 18690.0 11030.0 1474.0 6044.0 8508.0 8558.0 28348.0 1548.0 28198.0 9224.0 5075.0 2224.0 3873.0 163457.0 50143.0 213600.0 13111 6842 14266 15619 8209 23828 7424 12398 11592 9987 4368 1336 3034 10356 13731 12265 16128 16147 17763 4283 9399 10503 13473 213600
5 2015 18550 1920 4540 180 780 720 1340 485 770 2440 5750 16200 1390 3960 205 800 780 1240 550 970 2510 4920 625.3 2953.7 78.8 5.6 74.4 2328.3 145.30325 172.99950 209.96075 155.77475 165.05950 143.59000 150.65925 198.53075 137.80175 107.15200 132.47975 241.83900 129.02475 136.66400 171.36475 84.66750 188.76625 170.08400 129.65700 125.58400 142.62450 117.33000 215.30150 185.26900 139.21500 135.11000 226.86450 258.14750 122.26300 153.56575 220.05500 163.96100 173.75725 142.87150 167.60600 3470.0 9025.0 11745.0 13300.0 14057.5 14255.0 14037.5 13852.5 13335.0 12595.0 9360.0 12192.5 5555.0 81076.0 18754.0 11291.0 1541.0 6176.0 8329.0 8681.0 29588.0 1479.0 29434.0 9427.0 4841.0 2338.0 3996.0 170990.0 51516.0 222506.0 8240 6936 14531 18113 8753 26866 7595 13833 12486 10491 4763 1364 3420 11424 14452 13514 16526 17148 18570 4556 9824 10998 14263 222506
6 2016 18440 2120 5510 285 730 1000 1560 585 830 2820 5990 14920 2220 4270 285 700 800 1270 600 880 2130 5350 646.8 3022.5 78.6 5.6 74.2 2375.8 165.82600 216.45450 201.01200 185.86800 197.03000 155.69875 170.62350 227.02200 159.73250 118.36025 147.28575 278.17775 139.57950 144.00600 196.75375 84.84350 219.53000 196.57500 141.57025 154.53600 158.96325 124.94600 250.19975 201.40250 156.16900 144.60050 269.36675 312.98700 131.72300 172.76600 252.50325 181.38500 202.33150 157.08750 188.63650 3570.0 9267.5 11977.5 13605.0 14405.0 14702.5 14470.0 14267.5 13745.0 12967.5 9682.5 12505.0 5994.0 88139.0 19413.0 12287.0 1638.0 6444.0 7221.0 8944.0 30654.0 1380.0 30338.0 10057.0 4693.0 2440.0 4216.0 180735.0 53124.0 233859.0 7691 6107 13779 19703 9105 28808 7673 15170 12856 11083 5327 1560 3745 12430 15012 13962 17919 18106 19932 4889 10322 11431 15164 233859
7 2017 22050 2090 5140 265 950 1120 1640 655 910 2640 8000 15250 1710 4870 260 710 860 1200 590 630 2260 6650 608.7 3103.3 80.4 5.3 76.1 2494.7 175.23000 243.52650 211.83100 214.93775 250.45875 179.49775 211.60125 282.45575 195.68175 127.82375 148.99275 310.85125 149.96350 158.51150 214.95475 77.32675 279.99425 241.28300 147.70400 228.61125 170.49775 130.92325 269.98625 214.66600 163.59300 159.66025 348.40925 390.85125 139.53325 182.87725 272.59550 207.35825 232.79000 166.67750 216.22275 3822.5 9607.5 12285.0 13912.5 14750.0 15095.0 14952.5 14665.0 14147.5 13327.5 9995.0 12832.5 6428.0 93624.0 20709.0 13820.0 1707.0 7011.0 7279.0 9358.0 32240.0 1450.0 31189.0 10745.0 5122.0 2604.0 4492.0 192176.0 55602.0 247778.0 11108 6438 14320 17562 9820 27381 7882 16763 12843 12294 5947 1830 4213 12263 15447 14487 19690 19297 21780 5330 10902 11924 15905 247778
8 2018 22660 2360 6720 290 860 970 1720 590 880 2940 7220 17380 2060 5530 217 730 1150 1560 485 770 2680 6270 600.8 3165.2 81.0 4.8 77.1 2564.3 178.72850 283.24475 211.24675 258.77075 284.16925 210.15675 243.12150 327.30875 224.37100 139.30525 156.54725 319.92250 155.55325 189.20975 231.89250 86.00700 302.97500 271.96825 152.39075 249.15500 177.33800 140.40925 275.51950 234.19450 171.59100 166.48125 376.26800 453.76300 150.22225 187.26825 277.11225 222.08300 257.69600 175.38925 234.23950 4052.5 10072.5 12815.0 14397.5 15257.5 15665.0 15582.5 15247.5 14727.5 13915.0 10507.5 13357.5 6970.0 99794.0 22583.0 15106.0 1844.0 7567.0 8129.0 10123.0 33610.0 1632.0 33555.0 11754.0 5609.0 2798.0 4835.0 205727.0 60184.0 265911.0 12610 7422 15426 19546 10092 29638 8004 19009 13977 12896 6247 1922 4440 12909 15889 16262 20538 20772 23382 5728 11531 12275 16708 265911
9 2019 19170 2290 5700 270 900 980 1820 725 1010 3260 6840 20200 2130 4860 225 920 980 1630 690 920 2600 6690 608.7 3219.5 81.1 4.5 77.5 2610.9 170.89575 290.04425 197.05425 286.09400 286.96150 212.75400 235.37500 340.89400 224.38775 148.06575 152.72900 310.46125 152.83000 194.33425 226.29225 91.44775 286.35175 268.00600 147.67325 282.87050 173.58700 141.81725 267.49075 244.36825 169.20850 154.99575 349.33125 473.02500 150.12700 187.57650 273.23250 209.39475 263.28000 176.35950 226.13875 4252.5 10460.0 13295.0 14860.0 15780.0 16160.0 16220.0 15822.5 15237.5 14417.5 11020.0 13852.5 7238.0 105433.0 24349.0 16322.0 1999.0 7886.0 8245.0 10744.0 34849.0 1685.0 35066.0 12484.0 5932.0 2966.0 5162.0 217065.0 63295.0 280360.0 12655 7652 16141 19888 10567 30455 8489 20314 14394 13503 6471 2105 4609 13609 16734 17161 21988 22127 25041 6116 12279 13099 18030 280360
10 2020 20070 1950 5130 250 940 990 1470 680 1090 2800 6460 17190 1740 4740 175 750 1010 1410 570 1000 2400 5170 622.2 3272.4 81.0 4.3 77.5 2650.3 146.18475 271.09825 167.69850 316.91225 272.20025 218.50475 242.59325 294.45425 207.24825 139.34550 134.22625 288.20650 141.80250 193.32050 193.00775 77.95150 267.99125 261.99975 123.96100 272.39275 156.77425 128.82050 247.31050 216.38300 132.52175 130.04775 342.76125 454.21950 136.42800 165.02750 256.37650 181.03125 236.54725 154.66700 199.12575 4595.0 10627.5 13565.0 15062.5 15942.5 16417.5 16535.0 16147.5 15552.5 14747.5 11495.0 14167.5 7712.0 110312.0 25993.0 17283.0 2056.0 8397.0 8483.0 11344.0 37561.0 1725.0 37099.0 13097.0 6093.0 3135.0 5487.0 229139.0 66638.0 295777.0 13866 7383 16375 20524 10733 31257 8992 22400 14558 13983 6823 2140 4875 13817 17359 17594 23654 23810 27100 6289 13457 13795 19447 295777
11 2021 22980 3300 6180 270 1390 1090 1780 790 1160 3800 7750 16450 2340 4950 185 770 930 1390 660 860 2800 5470 643.7 3318.6 80.6 4.9 76.6 2674.9 189.51575 392.75500 248.80475 451.37950 381.41450 306.77775 344.60875 396.52100 291.07275 195.25350 177.00750 389.31900 197.62975 294.09700 233.62625 115.64900 401.49900 338.09400 157.81250 369.92700 191.84925 180.05150 329.80450 282.02625 194.41750 180.38150 487.17575 639.70600 183.62100 209.55175 336.20350 257.89600 337.49750 211.00625 282.83275 4875.0 11410.0 14522.5 16010.0 16915.0 17427.5 17552.5 17115.0 16440.0 15597.5 12175.0 15062.5 7804.0 113290.0 26954.0 18012.0 2256.0 8878.0 8077.0 12115.0 37606.0 1784.0 37011.0 12860.0 6180.0 3197.0 5582.0 234991.0 66613.0 301604.0 14319 6645 15200 19432 11376 30808 8555 23294 15958 15235 6002 1557 4255 11724 17244 18121 24686 25464 28022 5633 14219 14507 21356 301604
12 2022 23740 2460 5800 265 940 1130 1870 620 1160 3500 6770 17440 2070 4100 345 770 890 1510 570 1110 2720 5670 603.1 3310.2 81.8 3.6 78.8 2707.1 200.32850 429.89125 286.85100 459.61800 394.09675 342.01750 402.06400 457.42375 302.15075 192.75275 188.98575 391.05925 213.92500 333.00550 338.50825 107.83000 437.89625 342.50450 173.08300 368.58675 188.49375 186.20775 348.89025 362.15575 208.88800 201.23075 492.31925 695.99400 185.33725 241.53550 342.63600 281.00425 386.71325 217.79625 314.24425 5285.0 12522.5 15717.5 17320.0 18235.0 18725.0 18897.5 18440.0 17642.5 16700.0 13062.5 16185.0 8684.0 124270.0 29378.0 19527.0 2318.0 9773.0 8911.0 13110.0 40777.0 1798.0 40380.0 14016.0 7144.0 3283.0 6038.0 256748.0 72659.0 329407.0 16373 6805 16463 19567 12030 31597 9658 24320 18992 16985 6851 1743 4896 13124 18604 20252 26033 27240 31774 6671 15185 15489 23665 329407

2.4 Big Data Processing OptimizationΒΆ

'Job market' and 'GDP' related data have been merged into a single master table (merged_data). Given the size of this table, the following strategies are planned to optimise data processing performance:

  1. Convert to Dask DataFrame:
  • This approach greatly improves processing efficiency by distributing data processing across multiple cores.
  1. Changing data types:
  • Memory usage can be halved by converting float64,int64 types to float32,int32 respectively. In this way, calculation speed can be increased without loss of data accuracy.
  1. Store data in HDF5 format:
  • HDF5 is a high performance data format. Storing data in HDF5 not only increases read/write speeds, but also supports complex queries and efficient data access.

2.4.1 Convert to Dask DataFrameΒΆ

InΒ [61]:
import dask.dataframe as dd

# Convert to Dask DataFrame
ddf = dd.from_pandas(merged_data, npartitions=10)  

2.4.2 Changing data typesΒΆ

InΒ [62]:
# Change float64 to float32. Also change int64 to int32.
for column in merged_data.columns:
    if merged_data[column].dtype == 'float64':
        merged_data[column] = merged_data[column].astype('float32')
    if merged_data[column].dtype == 'int64':
        merged_data[column] = merged_data[column].astype('int32')

2.4.3 Store data in HDF5 formatΒΆ

It is important to ensure that the column names do not contain any special characters before saving the dataset in HDF5 format.
Therefore, the first step is to adjust the column names.

InΒ [63]:
# Define a function to replace special characters in column names
def clean_column_names(column_name):
    return column_name.replace(' ', '_').replace('/', '_').replace("'", "").replace('&', 'and').replace(',', '_').replace('-', '_')

# Apply this function to all columns
merged_data.columns = [clean_column_names(col) for col in merged_data.columns]

After changing all column names, the next step is saving the file in HDF5

InΒ [64]:
# Save dataset to HDF5 file
merged_data.to_hdf('merged_data.h5', key='data', mode='w', format='table', data_columns=True)

3. Data AnalysisΒΆ

3.1 Discuss the overall GDP changes in New Zealand from 2010 to 2022. What are the top three industrial contributions?ΒΆ

3.1.1 GDP Distribution in North Island, South Island and New Zealand from 2010 to 2022ΒΆ

InΒ [65]:
sns.set_theme(style="whitegrid")

plot_data = pd.melt(merged_data, id_vars=['Year'], value_vars=['GDP_Total_North_Island', 'GDP_Total_South_Island', 'GDP_New_Zealand'], var_name='Region', value_name='GDP')
plt.figure(figsize=(14, 7))
sns.lineplot(data=plot_data, x='Year', y='GDP', hue='Region', linewidth=2.5)

plt.xlabel('Year')
plt.ylabel('GDP')
plt.title('GDP Distribution in North Island, South Island and New Zealand from 2010 to 2022')
plt.figtext(0.95, 0.02, 'Source: GDP_region.csv', horizontalalignment='right', verticalalignment='bottom')
Out[65]:
Text(0.95, 0.02, 'Source: GDP_region.csv')
No description has been provided for this image

The above table shows that GDP in New Zealand has been increasing year by year since 2010, while the GDP of the North Island seems to be higher than the South Island, the gap seems to be very significant.

It may because Auckland and Wellington and other big cities are located in the North Island.

3.1.2 GDP Distribution by Industry in New Zealand from 2010 to 2022ΒΆ

InΒ [66]:
columns_choose2 = [
    'GDP_Agriculture',
    'GDP_Forestry__Fishing__and_Mining',
    'GDP_Forestry__Fishing__Mining__Electricity__Gas__Water_and_Waste_Services',
    'GDP_Manufacturing',
    'GDP_Electricity__Gas__Water__and_Waste_services',
    'GDP_Construction',
    'GDP_Wholesale_Trade',
    'GDP_Retail_Trade',
    'GDP_Accommodation_and_Food_Services',
    'GDP_Accommodation',
    'GDP_Food_and_beverage_services',
    'GDP_Transport__Postal_and_Warehousing',
    'GDP_Information_Media__Telecommunications_and_Other_Services',
    'GDP_Financial_and_Insurance_Services',
    'GDP_Rental__Hiring_and_Real_Estate_Services',
    'GDP_Owner_Occupied_Property_Operation',
    'GDP_Professional__Scientific__and_Technical_Services',
    'GDP_Administrative_and_Support_Services',
    'GDP_Public_Administration_and_Safety',
    'GDP_Education_and_Training',
    'GDP_Health_Care_and_Social_Assistance']

years = merged_data['Year']
vals = [merged_data[col].values for col in columns_choose2]

plt.figure(figsize=(16, 9), dpi=80)
colors = [plt.cm.Spectral(i / float(len(columns_choose2) - 1)) for i in range(len(columns_choose2))]

plt.stackplot(years, vals, labels=columns_choose2, colors=colors)
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), frameon=True)
plt.title('GDP Distribution by Industry in New Zealand from 2010 to 2022', fontsize=22)
plt.xlabel('Year')
plt.ylabel('GDP')
plt.xticks(rotation=45)
plt.figtext(0.95, 0.02, 'Source: industryGDP.csv', horizontalalignment='right', verticalalignment='bottom')
Out[66]:
Text(0.95, 0.02, 'Source: industryGDP.csv')
No description has been provided for this image

According to the chart, we can see that there are 24 industries in the industry GDP file, manufacturing and professional scientific and technical services seem to have the largest proportion.

The whole industry seems to be growing steadily, and there is no particularly huge decline or rise.

3.1.3 Finding the Top 3 Contribution Industries to New Zealand GDP for 2022-2023ΒΆ

InΒ [67]:
plt.figure(figsize=(14, 5))

plt.plot(merged_industrycontribution['Industry'], merged_industrycontribution['GDP_2022(M)'], label='Contribution of GDP 2022')
plt.plot(merged_industrycontribution['Industry'], merged_industrycontribution['GDP_2023(M)'], label='Contribution of GDP 2023')
average_gdp_2022 = merged_industrycontribution['GDP_2022(M)'].mean()
average_gdp_2023 = merged_industrycontribution['GDP_2023(M)'].mean()

plt.axhline(y=average_gdp_2022, color='skyblue', linestyle='--', label='Average GDP 2022')
plt.axhline(y=average_gdp_2023, color='orange', linestyle='--', label='Average GDP 2023')

plt.ylabel('GDP')
plt.title("Contribution of Each Industry to New Zealand GDP in 2022-2023")
plt.xticks(rotation=45,fontsize=9)
plt.legend()
plt.subplots_adjust(bottom=0.30, right=0.85)
plt.figtext(0.95, 0.02, 'Source: API https://rep.infometrics.co.nz ', horizontalalignment='right', verticalalignment='bottom')
Out[67]:
Text(0.95, 0.02, 'Source: API https://rep.infometrics.co.nz ')
No description has been provided for this image

Based on the table, we can identify the following insights about the GDP contributions for New Zealand in 2022-2023. The figures for these two years are very close, indicating that there have been no significant changes across the various industries.

The highest contributing industry is the professional, scientific, and technical industry. The second highest is manufacturing. The third highest is health care and social assistance.

The lowest contributing industries are mining and art, recreation, likely due to the lack of mineral resources in New Zealand.

In terms of the median contribution, agriculture, forestry, and fishing and Public administration and safety are in the middle range, indicating that their contribution to New Zealand's GDP is moderate, not too high and not too low.

We found the highest contributing industry is the professional, scientific, and technical industry, which we found very interesting because it is also the field we are currently studying. Our subsequent analysis will continue to explore more aspects.

3.2 Does the Industry that Contributes the Most GDP Bring the Same Proportion of Job Opportunities?ΒΆ

3.2.1 Adding Employment Job Numbers Variable and Analyzing Their Relationship to GDP ProportionΒΆ

InΒ [68]:
plt.figure(figsize=(14, 5))

plt.plot(merged_industrycontribution['Industry'], merged_industrycontribution['GDP_2022(M)'], label='Contribution of GDP 2022')
plt.plot(merged_industrycontribution['Industry'], merged_industrycontribution['GDP_2023(M)'], label='Contribution of GDP 2023')
plt.plot(merged_industrycontribution['Industry'], merged_industrycontribution['Employment_2022'],color='Green', linestyle='--', label='Jobs Numbers 2022')
plt.plot(merged_industrycontribution['Industry'], merged_industrycontribution['Employment_2023'], color='Green',label='Jobs Numbers 2023')

plt.title("Contribution of Each Industry to New Zealand GDP and Job Numbers in 2022-2023")
plt.xticks(rotation=45,fontsize=9)
plt.legend(fontsize=10, loc='best', bbox_to_anchor=(1, 1))
plt.subplots_adjust(bottom=0.30, right=0.85)
plt.figtext(0.95, 0.02, 'Source: API https://rep.infometrics.co.nz ', horizontalalignment='right', verticalalignment='bottom')
Out[68]:
Text(0.95, 0.02, 'Source: API https://rep.infometrics.co.nz ')
No description has been provided for this image

We found something very interesting! The top three contributing industries to GDP are Professional, scientific, and technical services, Manufacturing, and Health care and social assistance.However, these do not directly correspond to the industries providing the most job opportunities. From the table, we can see that the industries providing the most jobs are Construction, Health Care and Social Assistance, and Professional, Scientific, and Technical Services.

This indicates that there is not a direct correlation between industry GDP contributions and the number of job opportunities in New Zealand. There are several possible reasons why Professional, Scientific, and Technical Services, as well as Manufacturing, contribute more to GDP: Professional, Scientific, and Technical Services and Manufacturing industries typically rely on high skills and high productivity. They may have higher capital investment and technical equipment, enabling each employee to generate more value. Professionals usually have high levels of skill and knowledge, resulting in higher wages. Higher wages translate into higher GDP contributions. Manufacturing is highly dependent on the export market. The products produced may be sold globally, bringing in significant income and GDP contribution, rather than relying solely on the local market.

When examining the relationship between GDP and employment in each sector, we noticed a particular trend:
Transportation, Postal, and Warehousing contribute significantly to GDP, but the number of jobs in these sectors is very low. This may be due to automation and technological advancements that have increased productivity, thus significantly reducing the need for human labor.

3.3 Based on the Occupation Listed on Careers.NZ. What's the Job Opportunities in 6 Main Industries?ΒΆ

3.3.1 Job Opportunities by 6 Main Industries in New Zealand (Lastest DATA)ΒΆ

InΒ [69]:
summary_table
Out[69]:
Job Opportunities Average Good Poor
Industry
Construction and infrastructure 16 40 3
Creative industries 22 7 29
Manufacturing and technology 25 64 11
Primary industries 22 34 3
Services industries 61 75 30
Social and community services 29 66 15
InΒ [70]:
plt.figure(figsize=(14, 7))

width = 0.3
x = np.arange(len(summary_table.index))

plt.bar(x - width, summary_table['Good'], width=width, label='Good',color='tan')
plt.bar(x, summary_table['Average'], width=width, label='Average',color='peru')
plt.bar(x + width, summary_table['Poor'], width=width, label='Poor',color='maroon')

plt.xlabel('Vocations')
plt.ylabel('Classified Numbers')
plt.title(' Job Opportunities by 6 Main Industries in New Zealand')
plt.xticks(x, summary_table.index, rotation=45, ha='right', fontsize=12)
plt.legend(title='Job Opportunities')

plt.subplots_adjust(bottom=0.30, right=0.85)
plt.figtext(0.95, 0.02, 'Source: Web Scraping https://www.careers.govt.nz', horizontalalignment='right', verticalalignment='bottom')
Out[70]:
Text(0.95, 0.02, 'Source: Web Scraping https://www.careers.govt.nz')
No description has been provided for this image

There are over 60 types of jobs categorized as "Good" in Manufacturing and Technology which is outstanding compared to other industries. Overall, the prospects for Manufacturing and Technology look quite promising.

Construction and Infrastructure seem to have fewer job types overall, but more than half are in the "Good" category.

The Creative Industry has fewer job types overall, and most of them fall into the "Poor" category, indicating that the prospects for the Creative Industry are somewhat bleak.

The Service Industry has the highest proportion of "Average" jobs and also the most job types overall.

3.3.2 Job Opportunities by 6 Main Industries in New Zealand (Lastest DATA) - AccumulatedΒΆ

InΒ [71]:
plt.figure(figsize=(14, 7))

colors = sns.color_palette(['tan', 'peru', 'maroon'])

p1 = plt.bar(summary_table.index, summary_table['Good'], color=colors[0], label='Good')
p2 = plt.bar(summary_table.index, summary_table['Average'], bottom=summary_table['Good'], color=colors[1], label='Average')
p3 = plt.bar(summary_table.index, summary_table['Poor'], bottom=summary_table['Good'] + summary_table['Average'], color=colors[2], label='Poor')

plt.xlabel('Vocations')
plt.ylabel('Classified Numbers')
plt.title('Job Opportunities by 6 Main Industries in New Zealand- Accumulated')
plt.xticks(rotation=45, ha='right', fontsize=12)
plt.legend(title='Job Opportunities')
plt.subplots_adjust(bottom=0.30, right=0.85)
plt.figtext(0.95, 0.02, 'Source: Web Scraping https://www.careers.govt.nz', horizontalalignment='right', verticalalignment='bottom')
Out[71]:
Text(0.95, 0.02, 'Source: Web Scraping https://www.careers.govt.nz')
No description has been provided for this image

Social and Community Service provides the second-highest number of job types. Interestingly, this aligns with the 3.2.1 table, which indicated that Social Assistance and Professional Services offer the second-most job opportunities.

β˜… Note: The above source listed in each graph shows various methods we used to collect data. From below graph, the source of each table's data will not be specifically indicated.ΒΆ

3.4 As the Largest City in New Zealand and a Major Economic Hub, is Auckland the Best in Terms of All Vacancy Indices (AVIs) and GDP from 2010 to 2022?ΒΆ

3.4.1 All Vacancy Indices (AVIs) by Region from 2010-2022ΒΆ

InΒ [72]:
job_online
Out[72]:
Year AVI_Auckland AVI_Bay of Plenty AVI_Canterbury AVI_Gisborne Hawke's Bay AVI_Marlborough/NelsonTasman/West Coast AVI_Manawatu/Wanganui-Taranaki AVI_Northland AVI_Otago Southland AVI_Waikato AVI_Wellington AVI_ids_Accounting AVI_ids_Construction AVI_ids_Education AVI_ids_Health AVI_ids_Hospitalty AVI_ids_IT AVI_ids_Manufacturing AVI_ids_Primary AVI_ids_Sales AVI_ids_Other AVI_ocp_Managers AVI_ocp_Professionals AVI_ocp_Tech & Trades AVI_ocp_Community & Personal Services AVI_ocp_Clerical & Administration AVI_ocp_Sales AVI_ocp_Machinery Drivers AVI_ocp_Labourers AVI_ocp_highly skilled AVI_ocp_skilled AVI_ocp_semi-skilled AVI_ocp_low skilled AVI_ocp_unskilled AVI_ocp_Skilled AVI_ocp_Unskilled
0 2010 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000 100.00000
1 2011 101.53725 103.78475 131.81150 107.87775 111.24925 104.01875 109.50200 116.04450 105.59650 101.32300 106.24775 115.75200 92.19875 104.26875 107.14425 102.21425 111.41325 123.85425 101.98475 108.94725 103.91725 106.69575 121.15200 115.34075 107.32600 105.28125 113.40850 118.31375 105.89775 103.69375 126.95700 111.03375 104.36725 108.66475 109.75675
2 2012 104.07175 110.63300 170.32750 120.65275 129.16900 117.80575 115.17250 138.57875 110.88150 102.74725 109.49900 147.46800 83.28000 119.27800 112.45725 97.90175 125.17300 148.83975 105.11225 113.70325 106.71575 107.81950 142.38200 133.33875 113.67750 107.55275 137.17150 145.01325 107.70150 110.10950 150.74725 120.93250 116.90500 113.88775 120.46675
3 2013 116.27650 130.56400 191.61625 127.15625 136.18075 137.26350 120.30400 150.94075 123.46825 106.19475 112.27775 184.80025 92.26350 123.59450 134.52225 92.37850 153.48125 158.91900 113.93700 122.01075 116.59175 108.56500 171.24675 157.54900 120.54600 118.16900 180.68500 190.80675 110.85300 123.29000 181.36625 137.46225 138.64800 123.33150 138.68850
4 2014 133.69175 154.13400 220.14950 143.16750 165.08475 144.57075 141.19150 188.93475 140.00225 111.70625 121.78200 230.17750 106.67700 129.58350 162.81525 95.91100 181.68300 183.42750 128.40525 121.79475 135.62400 115.74175 207.52175 180.00800 132.23875 131.47725 223.29900 249.28800 120.81950 142.89900 209.12425 158.86300 167.51250 138.88250 162.42250
5 2015 145.30325 172.99950 209.96075 155.77475 165.05950 143.59000 150.65925 198.53075 137.80175 107.15200 132.47975 241.83900 129.02475 136.66400 171.36475 84.66750 188.76625 170.08400 129.65700 125.58400 142.62450 117.33000 215.30150 185.26900 139.21500 135.11000 226.86450 258.14750 122.26300 153.56575 220.05500 163.96100 173.75725 142.87150 167.60600
6 2016 165.82600 216.45450 201.01200 185.86800 197.03000 155.69875 170.62350 227.02200 159.73250 118.36025 147.28575 278.17775 139.57950 144.00600 196.75375 84.84350 219.53000 196.57500 141.57025 154.53600 158.96325 124.94600 250.19975 201.40250 156.16900 144.60050 269.36675 312.98700 131.72300 172.76600 252.50325 181.38500 202.33150 157.08750 188.63650
7 2017 175.23000 243.52650 211.83100 214.93775 250.45875 179.49775 211.60125 282.45575 195.68175 127.82375 148.99275 310.85125 149.96350 158.51150 214.95475 77.32675 279.99425 241.28300 147.70400 228.61125 170.49775 130.92325 269.98625 214.66600 163.59300 159.66025 348.40925 390.85125 139.53325 182.87725 272.59550 207.35825 232.79000 166.67750 216.22275
8 2018 178.72850 283.24475 211.24675 258.77075 284.16925 210.15675 243.12150 327.30875 224.37100 139.30525 156.54725 319.92250 155.55325 189.20975 231.89250 86.00700 302.97500 271.96825 152.39075 249.15500 177.33800 140.40925 275.51950 234.19450 171.59100 166.48125 376.26800 453.76300 150.22225 187.26825 277.11225 222.08300 257.69600 175.38925 234.23950
9 2019 170.89575 290.04425 197.05425 286.09400 286.96150 212.75400 235.37500 340.89400 224.38775 148.06575 152.72900 310.46125 152.83000 194.33425 226.29225 91.44775 286.35175 268.00600 147.67325 282.87050 173.58700 141.81725 267.49075 244.36825 169.20850 154.99575 349.33125 473.02500 150.12700 187.57650 273.23250 209.39475 263.28000 176.35950 226.13875
10 2020 146.18475 271.09825 167.69850 316.91225 272.20025 218.50475 242.59325 294.45425 207.24825 139.34550 134.22625 288.20650 141.80250 193.32050 193.00775 77.95150 267.99125 261.99975 123.96100 272.39275 156.77425 128.82050 247.31050 216.38300 132.52175 130.04775 342.76125 454.21950 136.42800 165.02750 256.37650 181.03125 236.54725 154.66700 199.12575
11 2021 189.51575 392.75500 248.80475 451.37950 381.41450 306.77775 344.60875 396.52100 291.07275 195.25350 177.00750 389.31900 197.62975 294.09700 233.62625 115.64900 401.49900 338.09400 157.81250 369.92700 191.84925 180.05150 329.80450 282.02625 194.41750 180.38150 487.17575 639.70600 183.62100 209.55175 336.20350 257.89600 337.49750 211.00625 282.83275
12 2022 200.32850 429.89125 286.85100 459.61800 394.09675 342.01750 402.06400 457.42375 302.15075 192.75275 188.98575 391.05925 213.92500 333.00550 338.50825 107.83000 437.89625 342.50450 173.08300 368.58675 188.49375 186.20775 348.89025 362.15575 208.88800 201.23075 492.31925 695.99400 185.33725 241.53550 342.63600 281.00425 386.71325 217.79625 314.24425
13 2023 150.42225 316.88425 226.37375 391.14925 318.20275 273.39400 333.16950 376.89850 230.76125 149.42500 153.03725 293.48275 225.12700 291.17150 242.09025 70.66075 303.14050 281.37575 130.16425 284.00675 153.23075 154.13150 261.71650 287.60175 159.32575 149.50925 355.24425 458.83100 148.58200 205.38975 265.21225 207.04550 266.91375 175.83525 226.94200
14 2024 125.56600 265.24100 185.86700 368.91800 266.95100 231.45900 293.04300 332.75100 198.37200 110.94500 127.45200 246.27800 208.19800 265.36500 205.14200 52.28700 234.85900 236.81800 109.07700 226.68000 131.48600 128.65100 227.27200 241.57000 132.47300 126.55200 280.04200 350.80000 122.93600 184.20400 227.88300 169.97600 208.36100 147.56200 182.64700
InΒ [73]:
_ = job_online.hist(bins=int(np.sqrt(len(job_online))),facecolor='grey', figsize=(18, 18), xlabelsize=8)
No description has been provided for this image

Showing the freqeuncy of AVI dataset, giving the overall idea about how the data distributed.

InΒ [74]:
avi_columns = ['AVI_Auckland', 'AVI_Bay_of_Plenty', 'AVI_Canterbury', 'AVI_Gisborne_Hawkes_Bay',
    'AVI_Marlborough_NelsonTasman_West_Coast', 'AVI_Manawatu_Wanganui_Taranaki',
    'AVI_Northland', 'AVI_Otago_Southland', 'AVI_Waikato', 'AVI_Wellington']

plot_data = merged_data.melt(id_vars=['Year'], value_vars=avi_columns, var_name='Variable', value_name='Value')

g = sns.relplot(x='Year', y='Value', hue='Variable', size='Value', sizes=(40, 400), alpha=.5, palette="bright", height=6, data=plot_data, aspect=2)
g.set_axis_labels("Year", "AVI")
g.fig.suptitle("All Vacancy Indices (AVIs) by Region from 2010-2022", fontsize=16)
C:\Users\Alene\anaconda3\Lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight
  self._figure.tight_layout(*args, **kwargs)
Out[74]:
Text(0.5, 0.98, 'All Vacancy Indices (AVIs) by Region from 2010-2022')
No description has been provided for this image

The All Vacancies Indices (AVI) is an important indicator of the labor market. A higher AVI indicates more job vacancies, which means a better job market with more opportunities. Conversely, a lower AVI might indicate fewer job vacancies. Therefore, a higher AVI is typically considered indicative of a healthier job market. In 2010, all AVIs were set to 100 to make it easier to understand the growth or decline for each region in the following years.

Based on the above graph, we can observe that the blue dots representing Auckland's AVI are located around 100 to 200 from 2012 to 2022. Compared to other regions, Auckland does not have the best performance. This might be because the number of jobs in Auckland started from a larger baseline than other regions.

For 2022, the highest AVIs are seen in Bay of Plenty, Gisborne Hawke's Bay, and Otago Southland. This indicates that these regions have significantly increased their job opportunities since 2010.

3.4.2 GDP Contribution by Region from 2010-2022ΒΆ

InΒ [75]:
columns_with_index = {i: col for i, col in enumerate(merged_data.columns)}
for index, column in columns_with_index.items():
    print(f"Column {index}: {column}")
Column 0: Year
Column 1: job_creation_Auckland
Column 2: job_creation_Bay_of_Plenty
Column 3: job_creation_Canterbury
Column 4: job_creation_Gisborne
Column 5: job_creation_Hawkes_Bay
Column 6: job_creation_Manawatu_Wanganui
Column 7: job_creation_Otago
Column 8: job_creation_Taranaki
Column 9: job_creation_Tasman__Nelson__Marlborough__West_Coast
Column 10: job_creation_Waikato
Column 11: job_creation_Wellington
Column 12: job_destruction_Auckland
Column 13: job_destruction_Bay_of_Plenty
Column 14: job_destruction_Canterbury
Column 15: job_destruction_Gisborne
Column 16: job_destruction_Hawkes_Bay
Column 17: job_destruction_Manawatu_Wanganui
Column 18: job_destruction_Otago
Column 19: job_destruction_Taranaki
Column 20: job_destruction_Tasman__Nelson__Marlborough__West_Coast
Column 21: job_destruction_Waikato
Column 22: job_destruction_Wellington
Column 23: Not_in_Labour_Force
Column 24: Working_Age_Population
Column 25: Labour_Force_Participation_Rate
Column 26: Unemployment_Rate
Column 27: Employment_Rate
Column 28: Total_Labour_Force
Column 29: AVI_Auckland
Column 30: AVI_Bay_of_Plenty
Column 31: AVI_Canterbury
Column 32: AVI_Gisborne_Hawkes_Bay
Column 33: AVI_Marlborough_NelsonTasman_West_Coast
Column 34: AVI_Manawatu_Wanganui_Taranaki
Column 35: AVI_Northland
Column 36: AVI_Otago_Southland
Column 37: AVI_Waikato
Column 38: AVI_Wellington
Column 39: AVI_ids_Accounting
Column 40: AVI_ids_Construction
Column 41: AVI_ids_Education
Column 42: AVI_ids_Health
Column 43: AVI_ids_Hospitalty
Column 44: AVI_ids_IT
Column 45: AVI_ids_Manufacturing
Column 46: AVI_ids_Primary
Column 47: AVI_ids_Sales
Column 48: AVI_ids_Other
Column 49: AVI_ocp_Managers
Column 50: AVI_ocp_Professionals
Column 51: AVI_ocp_Tech_and_Trades
Column 52: AVI_ocp_Community_and_Personal_Services
Column 53: AVI_ocp_Clerical_and_Administration
Column 54: AVI_ocp_Sales
Column 55: AVI_ocp_Machinery_Drivers
Column 56: AVI_ocp_Labourers
Column 57: AVI_ocp_highly_skilled
Column 58: AVI_ocp_skilled
Column 59: AVI_ocp_semi_skilled
Column 60: AVI_ocp_low_skilled
Column 61: AVI_ocp_unskilled
Column 62: AVI_ocp_Skilled
Column 63: AVI_ocp_Unskilled
Column 64: median_wage_15_19
Column 65: median_wage_20_24
Column 66: median_wage_25_29
Column 67: median_wage_30_34
Column 68: median_wage_35_39
Column 69: median_wage_40_44
Column 70: median_wage_45_49
Column 71: median_wage_50_54
Column 72: median_wage_55_59
Column 73: median_wage_60_64
Column 74: median_wage_65
Column 75: median_wage_Total_All_Ages
Column 76: GDP_Northland
Column 77: GDP_Auckland
Column 78: GDP_Waikato
Column 79: GDP_Bay_of_Plenty
Column 80: GDP_Gisborne
Column 81: GDP_Hawkes_Bay
Column 82: GDP_Taranaki
Column 83: GDP_Manawatu_Whanganui
Column 84: GDP_Wellington
Column 85: GDP_West_Coast
Column 86: GDP_Canterbury
Column 87: GDP_Otago
Column 88: GDP_Southland
Column 89: GDP_Marlborough
Column 90: GDP_Tasman_Nelson
Column 91: GDP_Total_North_Island
Column 92: GDP_Total_South_Island
Column 93: GDP_New_Zealand
Column 94: GDP_Agriculture
Column 95: GDP_Forestry__Fishing__and_Mining
Column 96: GDP_Forestry__Fishing__Mining__Electricity__Gas__Water_and_Waste_Services
Column 97: GDP_Primary_Manufacturing
Column 98: GDP_Other_Manufacturing
Column 99: GDP_Manufacturing
Column 100: GDP_Electricity__Gas__Water__and_Waste_services
Column 101: GDP_Construction
Column 102: GDP_Wholesale_Trade
Column 103: GDP_Retail_Trade
Column 104: GDP_Accommodation_and_Food_Services
Column 105: GDP_Accommodation
Column 106: GDP_Food_and_beverage_services
Column 107: GDP_Transport__Postal_and_Warehousing
Column 108: GDP_Information_Media__Telecommunications_and_Other_Services
Column 109: GDP_Financial_and_Insurance_Services
Column 110: GDP_Rental__Hiring_and_Real_Estate_Services
Column 111: GDP_Owner_Occupied_Property_Operation
Column 112: GDP_Professional__Scientific__and_Technical_Services
Column 113: GDP_Administrative_and_Support_Services
Column 114: GDP_Public_Administration_and_Safety
Column 115: GDP_Education_and_Training
Column 116: GDP_Health_Care_and_Social_Assistance
Column 117: GDP_Total_All_Industries
InΒ [76]:
columns_choose = [77, 76, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90]

years = merged_data['Year']
vals = [merged_data.iloc[:, col].values for col in columns_choose]
labels = [merged_data.columns[col] for col in columns_choose]

plt.figure(figsize=(16, 9), dpi=80)
colors = [plt.cm.Spectral(i / float(len(columns_choose) - 1)) for i in range(len(columns_choose))]

plt.stackplot(years, vals, labels=labels, colors=colors)
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), frameon=True)
plt.title('GDP Contribution by Region from 2010 to 2022', fontsize=22)
plt.xlabel('Year')
plt.ylabel('GDP')
plt.xticks(rotation=45)
Out[76]:
(array([2008., 2010., 2012., 2014., 2016., 2018., 2020., 2022., 2024.]),
 [Text(2008.0, 0, '2008'),
  Text(2010.0, 0, '2010'),
  Text(2012.0, 0, '2012'),
  Text(2014.0, 0, '2014'),
  Text(2016.0, 0, '2016'),
  Text(2018.0, 0, '2018'),
  Text(2020.0, 0, '2020'),
  Text(2022.0, 0, '2022'),
  Text(2024.0, 0, '2024')])
No description has been provided for this image

Based on GDP contribution graph, Auckland has consistently maintained the highest contribution proportion from 2010 to 2022. As New Zealand's main economic city, Auckland's stable economic growth is supported by the data.

From the above 2 graphs suggest that while Auckland remains crucial for the overall economy, other regions are catching up in terms of job market opportunities.

3.5 How Does Unemployment Rate in New Zealand Relate to Auckland's AVI and GDP?ΒΆ

3.5.1 Create the Classified Column for Unemployment Rate and AVI Auckland by Median.ΒΆ

InΒ [77]:
UnemploymentRate_median = merged_data['Unemployment_Rate'].median()
UnemploymentRate_median
Out[77]:
5.599999904632568
InΒ [78]:
AVI_Auckland_median = merged_data['AVI_Auckland'].median()
AVI_Auckland_median
Out[78]:
146.18475341796875
InΒ [79]:
unemployment_rate_label = np.where(merged_data['Unemployment_Rate'] > 5.6, 'High', 'Low')
avi_auckland_label = np.where(merged_data['AVI_Auckland'] > 146.18475, 'High', 'Low')
merged_data1 = merged_data.copy()
merged_data1 
Out[79]:
Year job_creation_Auckland job_creation_Bay_of_Plenty job_creation_Canterbury job_creation_Gisborne job_creation_Hawkes_Bay job_creation_Manawatu_Wanganui job_creation_Otago job_creation_Taranaki job_creation_Tasman__Nelson__Marlborough__West_Coast job_creation_Waikato job_creation_Wellington job_destruction_Auckland job_destruction_Bay_of_Plenty job_destruction_Canterbury job_destruction_Gisborne job_destruction_Hawkes_Bay job_destruction_Manawatu_Wanganui job_destruction_Otago job_destruction_Taranaki job_destruction_Tasman__Nelson__Marlborough__West_Coast job_destruction_Waikato job_destruction_Wellington Not_in_Labour_Force Working_Age_Population Labour_Force_Participation_Rate Unemployment_Rate Employment_Rate Total_Labour_Force AVI_Auckland AVI_Bay_of_Plenty AVI_Canterbury AVI_Gisborne_Hawkes_Bay AVI_Marlborough_NelsonTasman_West_Coast AVI_Manawatu_Wanganui_Taranaki AVI_Northland AVI_Otago_Southland AVI_Waikato AVI_Wellington AVI_ids_Accounting AVI_ids_Construction AVI_ids_Education AVI_ids_Health AVI_ids_Hospitalty AVI_ids_IT AVI_ids_Manufacturing AVI_ids_Primary AVI_ids_Sales AVI_ids_Other AVI_ocp_Managers AVI_ocp_Professionals AVI_ocp_Tech_and_Trades AVI_ocp_Community_and_Personal_Services AVI_ocp_Clerical_and_Administration AVI_ocp_Sales AVI_ocp_Machinery_Drivers AVI_ocp_Labourers AVI_ocp_highly_skilled AVI_ocp_skilled AVI_ocp_semi_skilled AVI_ocp_low_skilled AVI_ocp_unskilled AVI_ocp_Skilled AVI_ocp_Unskilled median_wage_15_19 median_wage_20_24 median_wage_25_29 median_wage_30_34 median_wage_35_39 median_wage_40_44 median_wage_45_49 median_wage_50_54 median_wage_55_59 median_wage_60_64 median_wage_65 median_wage_Total_All_Ages GDP_Northland GDP_Auckland GDP_Waikato GDP_Bay_of_Plenty GDP_Gisborne GDP_Hawkes_Bay GDP_Taranaki GDP_Manawatu_Whanganui GDP_Wellington GDP_West_Coast GDP_Canterbury GDP_Otago GDP_Southland GDP_Marlborough GDP_Tasman_Nelson GDP_Total_North_Island GDP_Total_South_Island GDP_New_Zealand GDP_Agriculture GDP_Forestry__Fishing__and_Mining GDP_Forestry__Fishing__Mining__Electricity__Gas__Water_and_Waste_Services GDP_Primary_Manufacturing GDP_Other_Manufacturing GDP_Manufacturing GDP_Electricity__Gas__Water__and_Waste_services GDP_Construction GDP_Wholesale_Trade GDP_Retail_Trade GDP_Accommodation_and_Food_Services GDP_Accommodation GDP_Food_and_beverage_services GDP_Transport__Postal_and_Warehousing GDP_Information_Media__Telecommunications_and_Other_Services GDP_Financial_and_Insurance_Services GDP_Rental__Hiring_and_Real_Estate_Services GDP_Owner_Occupied_Property_Operation GDP_Professional__Scientific__and_Technical_Services GDP_Administrative_and_Support_Services GDP_Public_Administration_and_Safety GDP_Education_and_Training GDP_Health_Care_and_Social_Assistance GDP_Total_All_Industries
0 2010 18130 1580 3320 250 830 840 1240 475 800 2730 4910 15380 1430 3470 195 850 980 1300 515 900 2270 5290 645.700012 2844.399902 77.300003 6.3 72.400002 2198.699951 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 3060.0 8050.0 10582.5 11865.0 12315.0 12137.5 12065.0 11905.0 11437.5 10515.0 7245.0 10632.5 4543.0 63382.0 15258.0 9409.0 1324.0 5150.0 7665.0 7544.0 25113.0 1351.0 22229.0 7894.0 4168.0 1794.0 3296.0 139388.0 40731.0 180119.0 8654 6651 12720 13001 7925 20926 6069 10464 9869 8292 3789 1226 2563 7929 12490 11119 13413 12192 14735 3633 8732 9204 11958 180119
1 2011 17310 1600 4100 190 710 810 1140 480 850 2220 5310 14540 1370 3730 190 690 890 1110 490 760 1920 5120 655.799988 2866.699951 77.099998 6.4 72.199997 2211.000000 101.537247 103.784752 131.811493 107.877747 111.249252 104.018753 109.501999 116.044502 105.596497 101.322998 106.247749 115.751999 92.198753 104.268753 107.144249 102.214249 111.413254 123.854248 101.984749 108.947250 103.917252 106.695747 121.152000 115.340752 107.325996 105.281250 113.408501 118.313751 105.897751 103.693748 126.957001 111.033752 104.367249 108.664749 109.756752 3152.5 8162.5 10835.0 12155.0 12712.5 12565.0 12462.5 12290.0 11845.0 10967.5 7752.5 10972.5 4842.0 65759.0 16075.0 9947.0 1394.0 5447.0 7977.0 7888.0 25501.0 1427.0 23219.0 8171.0 4498.0 1813.0 3446.0 144830.0 42574.0 187404.0 10643 6938 13291 13860 8191 22051 6353 10470 10081 8469 3857 1248 2609 8758 12421 9975 13648 13484 15529 3959 8835 9595 12337 187404
2 2012 17260 1560 4580 195 820 1040 1230 510 800 2710 5880 14410 1510 3710 160 920 940 1200 450 790 2290 5080 652.500000 2876.300049 77.300003 6.3 72.400002 2223.800049 104.071747 110.633003 170.327499 120.652748 129.169006 117.805748 115.172501 138.578751 110.881500 102.747253 109.499001 147.468002 83.279999 119.278000 112.457253 97.901749 125.172997 148.839752 105.112251 113.703247 106.715752 107.819504 142.382004 133.338745 113.677498 107.552750 137.171494 145.013245 107.701500 110.109497 150.747253 120.932503 116.904999 113.887749 120.466751 3257.5 8370.0 11112.5 12452.5 13102.5 13020.0 12925.0 12735.0 12280.0 11435.0 8245.0 11335.0 4922.0 68978.0 17102.0 10321.0 1432.0 5575.0 7896.0 7997.0 26724.0 1517.0 24316.0 8402.0 4599.0 1919.0 3503.0 150947.0 44256.0 195203.0 10622 6772 13371 15298 8017 23315 6600 10804 10526 8878 3940 1245 2696 9331 12960 10625 14757 14344 15869 4172 9041 9985 12665 195203
3 2013 18320 1800 4360 305 790 770 1290 660 830 2110 5820 13340 1470 3770 245 710 780 1070 480 760 1950 4720 666.900024 2878.800049 76.800003 6.6 71.800003 2211.899902 116.276497 130.563995 191.616257 127.156250 136.180756 137.263504 120.304001 150.940750 123.468246 106.194748 112.277748 184.800247 92.263496 123.594498 134.522247 92.378502 153.481247 158.919006 113.936996 122.010750 116.591751 108.565002 171.246750 157.548996 120.545998 118.168999 180.684998 190.806747 110.852997 123.290001 181.366257 137.462250 138.647995 123.331497 138.688507 3290.0 8555.0 11340.0 12705.0 13407.5 13420.0 13267.5 13065.0 12647.5 11850.0 8620.0 11617.5 4899.0 71351.0 16725.0 10490.0 1427.0 5730.0 7967.0 7999.0 27149.0 1449.0 25371.0 8526.0 4432.0 2001.0 3581.0 153739.0 45360.0 199099.0 9130 6610 13764 15133 7938 23071 7154 11541 10530 9489 4124 1270 2855 9790 13188 10921 15134 15260 16555 4165 9147 10154 13133 199099
4 2014 18870 1580 4710 285 690 860 1380 550 1010 2350 6050 15940 1620 3600 245 720 800 1180 600 690 2400 4680 641.799988 2899.399902 77.900002 6.0 73.199997 2257.600098 133.691757 154.134003 220.149506 143.167496 165.084747 144.570755 141.191498 188.934753 140.002243 111.706253 121.781998 230.177505 106.677002 129.583496 162.815247 95.911003 181.682999 183.427505 128.405243 121.794746 135.623993 115.741753 207.521744 180.007996 132.238754 131.477249 223.298996 249.287994 120.819504 142.899002 209.124252 158.863007 167.512497 138.882507 162.422501 3392.5 8820.0 11590.0 13025.0 13767.5 13877.5 13655.0 13475.0 13017.5 12245.0 9032.5 11925.0 5363.0 75444.0 18690.0 11030.0 1474.0 6044.0 8508.0 8558.0 28348.0 1548.0 28198.0 9224.0 5075.0 2224.0 3873.0 163457.0 50143.0 213600.0 13111 6842 14266 15619 8209 23828 7424 12398 11592 9987 4368 1336 3034 10356 13731 12265 16128 16147 17763 4283 9399 10503 13473 213600
5 2015 18550 1920 4540 180 780 720 1340 485 770 2440 5750 16200 1390 3960 205 800 780 1240 550 970 2510 4920 625.299988 2953.699951 78.800003 5.6 74.400002 2328.300049 145.303253 172.999496 209.960754 155.774750 165.059494 143.589996 150.659256 198.530746 137.801743 107.152000 132.479752 241.839005 129.024750 136.664001 171.364746 84.667503 188.766251 170.084000 129.656998 125.584000 142.624496 117.330002 215.301498 185.268997 139.214996 135.110001 226.864502 258.147491 122.263000 153.565750 220.054993 163.960999 173.757248 142.871506 167.606003 3470.0 9025.0 11745.0 13300.0 14057.5 14255.0 14037.5 13852.5 13335.0 12595.0 9360.0 12192.5 5555.0 81076.0 18754.0 11291.0 1541.0 6176.0 8329.0 8681.0 29588.0 1479.0 29434.0 9427.0 4841.0 2338.0 3996.0 170990.0 51516.0 222506.0 8240 6936 14531 18113 8753 26866 7595 13833 12486 10491 4763 1364 3420 11424 14452 13514 16526 17148 18570 4556 9824 10998 14263 222506
6 2016 18440 2120 5510 285 730 1000 1560 585 830 2820 5990 14920 2220 4270 285 700 800 1270 600 880 2130 5350 646.799988 3022.500000 78.599998 5.6 74.199997 2375.800049 165.826004 216.454498 201.011993 185.867996 197.029999 155.698746 170.623505 227.022003 159.732498 118.360252 147.285751 278.177765 139.579498 144.005997 196.753754 84.843498 219.529999 196.574997 141.570251 154.535995 158.963257 124.945999 250.199753 201.402496 156.169006 144.600494 269.366760 312.987000 131.723007 172.766006 252.503250 181.384995 202.331497 157.087494 188.636505 3570.0 9267.5 11977.5 13605.0 14405.0 14702.5 14470.0 14267.5 13745.0 12967.5 9682.5 12505.0 5994.0 88139.0 19413.0 12287.0 1638.0 6444.0 7221.0 8944.0 30654.0 1380.0 30338.0 10057.0 4693.0 2440.0 4216.0 180735.0 53124.0 233859.0 7691 6107 13779 19703 9105 28808 7673 15170 12856 11083 5327 1560 3745 12430 15012 13962 17919 18106 19932 4889 10322 11431 15164 233859
7 2017 22050 2090 5140 265 950 1120 1640 655 910 2640 8000 15250 1710 4870 260 710 860 1200 590 630 2260 6650 608.700012 3103.300049 80.400002 5.3 76.099998 2494.699951 175.229996 243.526505 211.830994 214.937744 250.458755 179.497757 211.601257 282.455750 195.681747 127.823753 148.992752 310.851257 149.963501 158.511505 214.954742 77.326752 279.994263 241.283005 147.703995 228.611252 170.497757 130.923248 269.986237 214.666000 163.593002 159.660248 348.409241 390.851257 139.533249 182.877243 272.595490 207.358246 232.789993 166.677505 216.222748 3822.5 9607.5 12285.0 13912.5 14750.0 15095.0 14952.5 14665.0 14147.5 13327.5 9995.0 12832.5 6428.0 93624.0 20709.0 13820.0 1707.0 7011.0 7279.0 9358.0 32240.0 1450.0 31189.0 10745.0 5122.0 2604.0 4492.0 192176.0 55602.0 247778.0 11108 6438 14320 17562 9820 27381 7882 16763 12843 12294 5947 1830 4213 12263 15447 14487 19690 19297 21780 5330 10902 11924 15905 247778
8 2018 22660 2360 6720 290 860 970 1720 590 880 2940 7220 17380 2060 5530 217 730 1150 1560 485 770 2680 6270 600.799988 3165.199951 81.000000 4.8 77.099998 2564.300049 178.728500 283.244751 211.246750 258.770752 284.169250 210.156754 243.121506 327.308746 224.371002 139.305252 156.547256 319.922485 155.553253 189.209747 231.892502 86.007004 302.975006 271.968262 152.390747 249.154999 177.337997 140.409256 275.519501 234.194504 171.591003 166.481247 376.268005 453.763000 150.222244 187.268250 277.112244 222.082993 257.696014 175.389252 234.239502 4052.5 10072.5 12815.0 14397.5 15257.5 15665.0 15582.5 15247.5 14727.5 13915.0 10507.5 13357.5 6970.0 99794.0 22583.0 15106.0 1844.0 7567.0 8129.0 10123.0 33610.0 1632.0 33555.0 11754.0 5609.0 2798.0 4835.0 205727.0 60184.0 265911.0 12610 7422 15426 19546 10092 29638 8004 19009 13977 12896 6247 1922 4440 12909 15889 16262 20538 20772 23382 5728 11531 12275 16708 265911
9 2019 19170 2290 5700 270 900 980 1820 725 1010 3260 6840 20200 2130 4860 225 920 980 1630 690 920 2600 6690 608.700012 3219.500000 81.099998 4.5 77.500000 2610.899902 170.895752 290.044250 197.054245 286.093994 286.961487 212.753998 235.375000 340.894012 224.387756 148.065750 152.729004 310.461243 152.830002 194.334244 226.292252 91.447746 286.351746 268.006012 147.673248 282.870514 173.587006 141.817245 267.490753 244.368256 169.208496 154.995743 349.331238 473.024994 150.126999 187.576508 273.232513 209.394745 263.279999 176.359497 226.138748 4252.5 10460.0 13295.0 14860.0 15780.0 16160.0 16220.0 15822.5 15237.5 14417.5 11020.0 13852.5 7238.0 105433.0 24349.0 16322.0 1999.0 7886.0 8245.0 10744.0 34849.0 1685.0 35066.0 12484.0 5932.0 2966.0 5162.0 217065.0 63295.0 280360.0 12655 7652 16141 19888 10567 30455 8489 20314 14394 13503 6471 2105 4609 13609 16734 17161 21988 22127 25041 6116 12279 13099 18030 280360
10 2020 20070 1950 5130 250 940 990 1470 680 1090 2800 6460 17190 1740 4740 175 750 1010 1410 570 1000 2400 5170 622.200012 3272.399902 81.000000 4.3 77.500000 2650.300049 146.184753 271.098236 167.698502 316.912262 272.200256 218.504745 242.593246 294.454254 207.248245 139.345505 134.226257 288.206512 141.802505 193.320496 193.007751 77.951500 267.991241 261.999756 123.960999 272.392761 156.774246 128.820496 247.310501 216.382996 132.521744 130.047745 342.761261 454.219513 136.427994 165.027496 256.376495 181.031250 236.547256 154.667007 199.125748 4595.0 10627.5 13565.0 15062.5 15942.5 16417.5 16535.0 16147.5 15552.5 14747.5 11495.0 14167.5 7712.0 110312.0 25993.0 17283.0 2056.0 8397.0 8483.0 11344.0 37561.0 1725.0 37099.0 13097.0 6093.0 3135.0 5487.0 229139.0 66638.0 295777.0 13866 7383 16375 20524 10733 31257 8992 22400 14558 13983 6823 2140 4875 13817 17359 17594 23654 23810 27100 6289 13457 13795 19447 295777
11 2021 22980 3300 6180 270 1390 1090 1780 790 1160 3800 7750 16450 2340 4950 185 770 930 1390 660 860 2800 5470 643.700012 3318.600098 80.599998 4.9 76.599998 2674.899902 189.515747 392.755005 248.804749 451.379486 381.414490 306.777740 344.608765 396.520996 291.072754 195.253494 177.007507 389.319000 197.629745 294.096985 233.626251 115.649002 401.498993 338.093994 157.812500 369.927002 191.849243 180.051498 329.804504 282.026245 194.417496 180.381500 487.175751 639.705994 183.621002 209.551743 336.203491 257.895996 337.497498 211.006256 282.832764 4875.0 11410.0 14522.5 16010.0 16915.0 17427.5 17552.5 17115.0 16440.0 15597.5 12175.0 15062.5 7804.0 113290.0 26954.0 18012.0 2256.0 8878.0 8077.0 12115.0 37606.0 1784.0 37011.0 12860.0 6180.0 3197.0 5582.0 234991.0 66613.0 301604.0 14319 6645 15200 19432 11376 30808 8555 23294 15958 15235 6002 1557 4255 11724 17244 18121 24686 25464 28022 5633 14219 14507 21356 301604
12 2022 23740 2460 5800 265 940 1130 1870 620 1160 3500 6770 17440 2070 4100 345 770 890 1510 570 1110 2720 5670 603.099976 3310.199951 81.800003 3.6 78.800003 2707.100098 200.328506 429.891235 286.851013 459.618011 394.096741 342.017487 402.063995 457.423737 302.150757 192.752747 188.985748 391.059265 213.925003 333.005493 338.508240 107.830002 437.896240 342.504486 173.082993 368.586761 188.493744 186.207748 348.890259 362.155762 208.888000 201.230743 492.319244 695.994019 185.337250 241.535507 342.635986 281.004242 386.713257 217.796249 314.244263 5285.0 12522.5 15717.5 17320.0 18235.0 18725.0 18897.5 18440.0 17642.5 16700.0 13062.5 16185.0 8684.0 124270.0 29378.0 19527.0 2318.0 9773.0 8911.0 13110.0 40777.0 1798.0 40380.0 14016.0 7144.0 3283.0 6038.0 256748.0 72659.0 329407.0 16373 6805 16463 19567 12030 31597 9658 24320 18992 16985 6851 1743 4896 13124 18604 20252 26033 27240 31774 6671 15185 15489 23665 329407
InΒ [80]:
unemployment_rate_label = np.where(merged_data['Unemployment_Rate'] > 5.6, 'High', 'Low')
avi_auckland_label = np.where(merged_data['AVI_Auckland'] > 146.18475, 'High', 'Low')

new_columns = pd.DataFrame({'Unemployment Rate Classified': unemployment_rate_label,'AVI Auckland Classified': avi_auckland_label})
merged_data = pd.concat([merged_data, new_columns], axis=1)
merged_data1 = merged_data.copy()

merged_data1
Out[80]:
Year job_creation_Auckland job_creation_Bay_of_Plenty job_creation_Canterbury job_creation_Gisborne job_creation_Hawkes_Bay job_creation_Manawatu_Wanganui job_creation_Otago job_creation_Taranaki job_creation_Tasman__Nelson__Marlborough__West_Coast job_creation_Waikato job_creation_Wellington job_destruction_Auckland job_destruction_Bay_of_Plenty job_destruction_Canterbury job_destruction_Gisborne job_destruction_Hawkes_Bay job_destruction_Manawatu_Wanganui job_destruction_Otago job_destruction_Taranaki job_destruction_Tasman__Nelson__Marlborough__West_Coast job_destruction_Waikato job_destruction_Wellington Not_in_Labour_Force Working_Age_Population Labour_Force_Participation_Rate Unemployment_Rate Employment_Rate Total_Labour_Force AVI_Auckland AVI_Bay_of_Plenty AVI_Canterbury AVI_Gisborne_Hawkes_Bay AVI_Marlborough_NelsonTasman_West_Coast AVI_Manawatu_Wanganui_Taranaki AVI_Northland AVI_Otago_Southland AVI_Waikato AVI_Wellington AVI_ids_Accounting AVI_ids_Construction AVI_ids_Education AVI_ids_Health AVI_ids_Hospitalty AVI_ids_IT AVI_ids_Manufacturing AVI_ids_Primary AVI_ids_Sales AVI_ids_Other AVI_ocp_Managers AVI_ocp_Professionals AVI_ocp_Tech_and_Trades AVI_ocp_Community_and_Personal_Services AVI_ocp_Clerical_and_Administration AVI_ocp_Sales AVI_ocp_Machinery_Drivers AVI_ocp_Labourers AVI_ocp_highly_skilled AVI_ocp_skilled AVI_ocp_semi_skilled AVI_ocp_low_skilled AVI_ocp_unskilled AVI_ocp_Skilled AVI_ocp_Unskilled median_wage_15_19 median_wage_20_24 median_wage_25_29 median_wage_30_34 median_wage_35_39 median_wage_40_44 median_wage_45_49 median_wage_50_54 median_wage_55_59 median_wage_60_64 median_wage_65 median_wage_Total_All_Ages GDP_Northland GDP_Auckland GDP_Waikato GDP_Bay_of_Plenty GDP_Gisborne GDP_Hawkes_Bay GDP_Taranaki GDP_Manawatu_Whanganui GDP_Wellington GDP_West_Coast GDP_Canterbury GDP_Otago GDP_Southland GDP_Marlborough GDP_Tasman_Nelson GDP_Total_North_Island GDP_Total_South_Island GDP_New_Zealand GDP_Agriculture GDP_Forestry__Fishing__and_Mining GDP_Forestry__Fishing__Mining__Electricity__Gas__Water_and_Waste_Services GDP_Primary_Manufacturing GDP_Other_Manufacturing GDP_Manufacturing GDP_Electricity__Gas__Water__and_Waste_services GDP_Construction GDP_Wholesale_Trade GDP_Retail_Trade GDP_Accommodation_and_Food_Services GDP_Accommodation GDP_Food_and_beverage_services GDP_Transport__Postal_and_Warehousing GDP_Information_Media__Telecommunications_and_Other_Services GDP_Financial_and_Insurance_Services GDP_Rental__Hiring_and_Real_Estate_Services GDP_Owner_Occupied_Property_Operation GDP_Professional__Scientific__and_Technical_Services GDP_Administrative_and_Support_Services GDP_Public_Administration_and_Safety GDP_Education_and_Training GDP_Health_Care_and_Social_Assistance GDP_Total_All_Industries Unemployment Rate Classified AVI Auckland Classified
0 2010 18130 1580 3320 250 830 840 1240 475 800 2730 4910 15380 1430 3470 195 850 980 1300 515 900 2270 5290 645.700012 2844.399902 77.300003 6.3 72.400002 2198.699951 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 3060.0 8050.0 10582.5 11865.0 12315.0 12137.5 12065.0 11905.0 11437.5 10515.0 7245.0 10632.5 4543.0 63382.0 15258.0 9409.0 1324.0 5150.0 7665.0 7544.0 25113.0 1351.0 22229.0 7894.0 4168.0 1794.0 3296.0 139388.0 40731.0 180119.0 8654 6651 12720 13001 7925 20926 6069 10464 9869 8292 3789 1226 2563 7929 12490 11119 13413 12192 14735 3633 8732 9204 11958 180119 High Low
1 2011 17310 1600 4100 190 710 810 1140 480 850 2220 5310 14540 1370 3730 190 690 890 1110 490 760 1920 5120 655.799988 2866.699951 77.099998 6.4 72.199997 2211.000000 101.537247 103.784752 131.811493 107.877747 111.249252 104.018753 109.501999 116.044502 105.596497 101.322998 106.247749 115.751999 92.198753 104.268753 107.144249 102.214249 111.413254 123.854248 101.984749 108.947250 103.917252 106.695747 121.152000 115.340752 107.325996 105.281250 113.408501 118.313751 105.897751 103.693748 126.957001 111.033752 104.367249 108.664749 109.756752 3152.5 8162.5 10835.0 12155.0 12712.5 12565.0 12462.5 12290.0 11845.0 10967.5 7752.5 10972.5 4842.0 65759.0 16075.0 9947.0 1394.0 5447.0 7977.0 7888.0 25501.0 1427.0 23219.0 8171.0 4498.0 1813.0 3446.0 144830.0 42574.0 187404.0 10643 6938 13291 13860 8191 22051 6353 10470 10081 8469 3857 1248 2609 8758 12421 9975 13648 13484 15529 3959 8835 9595 12337 187404 High Low
2 2012 17260 1560 4580 195 820 1040 1230 510 800 2710 5880 14410 1510 3710 160 920 940 1200 450 790 2290 5080 652.500000 2876.300049 77.300003 6.3 72.400002 2223.800049 104.071747 110.633003 170.327499 120.652748 129.169006 117.805748 115.172501 138.578751 110.881500 102.747253 109.499001 147.468002 83.279999 119.278000 112.457253 97.901749 125.172997 148.839752 105.112251 113.703247 106.715752 107.819504 142.382004 133.338745 113.677498 107.552750 137.171494 145.013245 107.701500 110.109497 150.747253 120.932503 116.904999 113.887749 120.466751 3257.5 8370.0 11112.5 12452.5 13102.5 13020.0 12925.0 12735.0 12280.0 11435.0 8245.0 11335.0 4922.0 68978.0 17102.0 10321.0 1432.0 5575.0 7896.0 7997.0 26724.0 1517.0 24316.0 8402.0 4599.0 1919.0 3503.0 150947.0 44256.0 195203.0 10622 6772 13371 15298 8017 23315 6600 10804 10526 8878 3940 1245 2696 9331 12960 10625 14757 14344 15869 4172 9041 9985 12665 195203 High Low
3 2013 18320 1800 4360 305 790 770 1290 660 830 2110 5820 13340 1470 3770 245 710 780 1070 480 760 1950 4720 666.900024 2878.800049 76.800003 6.6 71.800003 2211.899902 116.276497 130.563995 191.616257 127.156250 136.180756 137.263504 120.304001 150.940750 123.468246 106.194748 112.277748 184.800247 92.263496 123.594498 134.522247 92.378502 153.481247 158.919006 113.936996 122.010750 116.591751 108.565002 171.246750 157.548996 120.545998 118.168999 180.684998 190.806747 110.852997 123.290001 181.366257 137.462250 138.647995 123.331497 138.688507 3290.0 8555.0 11340.0 12705.0 13407.5 13420.0 13267.5 13065.0 12647.5 11850.0 8620.0 11617.5 4899.0 71351.0 16725.0 10490.0 1427.0 5730.0 7967.0 7999.0 27149.0 1449.0 25371.0 8526.0 4432.0 2001.0 3581.0 153739.0 45360.0 199099.0 9130 6610 13764 15133 7938 23071 7154 11541 10530 9489 4124 1270 2855 9790 13188 10921 15134 15260 16555 4165 9147 10154 13133 199099 High Low
4 2014 18870 1580 4710 285 690 860 1380 550 1010 2350 6050 15940 1620 3600 245 720 800 1180 600 690 2400 4680 641.799988 2899.399902 77.900002 6.0 73.199997 2257.600098 133.691757 154.134003 220.149506 143.167496 165.084747 144.570755 141.191498 188.934753 140.002243 111.706253 121.781998 230.177505 106.677002 129.583496 162.815247 95.911003 181.682999 183.427505 128.405243 121.794746 135.623993 115.741753 207.521744 180.007996 132.238754 131.477249 223.298996 249.287994 120.819504 142.899002 209.124252 158.863007 167.512497 138.882507 162.422501 3392.5 8820.0 11590.0 13025.0 13767.5 13877.5 13655.0 13475.0 13017.5 12245.0 9032.5 11925.0 5363.0 75444.0 18690.0 11030.0 1474.0 6044.0 8508.0 8558.0 28348.0 1548.0 28198.0 9224.0 5075.0 2224.0 3873.0 163457.0 50143.0 213600.0 13111 6842 14266 15619 8209 23828 7424 12398 11592 9987 4368 1336 3034 10356 13731 12265 16128 16147 17763 4283 9399 10503 13473 213600 High Low
5 2015 18550 1920 4540 180 780 720 1340 485 770 2440 5750 16200 1390 3960 205 800 780 1240 550 970 2510 4920 625.299988 2953.699951 78.800003 5.6 74.400002 2328.300049 145.303253 172.999496 209.960754 155.774750 165.059494 143.589996 150.659256 198.530746 137.801743 107.152000 132.479752 241.839005 129.024750 136.664001 171.364746 84.667503 188.766251 170.084000 129.656998 125.584000 142.624496 117.330002 215.301498 185.268997 139.214996 135.110001 226.864502 258.147491 122.263000 153.565750 220.054993 163.960999 173.757248 142.871506 167.606003 3470.0 9025.0 11745.0 13300.0 14057.5 14255.0 14037.5 13852.5 13335.0 12595.0 9360.0 12192.5 5555.0 81076.0 18754.0 11291.0 1541.0 6176.0 8329.0 8681.0 29588.0 1479.0 29434.0 9427.0 4841.0 2338.0 3996.0 170990.0 51516.0 222506.0 8240 6936 14531 18113 8753 26866 7595 13833 12486 10491 4763 1364 3420 11424 14452 13514 16526 17148 18570 4556 9824 10998 14263 222506 Low Low
6 2016 18440 2120 5510 285 730 1000 1560 585 830 2820 5990 14920 2220 4270 285 700 800 1270 600 880 2130 5350 646.799988 3022.500000 78.599998 5.6 74.199997 2375.800049 165.826004 216.454498 201.011993 185.867996 197.029999 155.698746 170.623505 227.022003 159.732498 118.360252 147.285751 278.177765 139.579498 144.005997 196.753754 84.843498 219.529999 196.574997 141.570251 154.535995 158.963257 124.945999 250.199753 201.402496 156.169006 144.600494 269.366760 312.987000 131.723007 172.766006 252.503250 181.384995 202.331497 157.087494 188.636505 3570.0 9267.5 11977.5 13605.0 14405.0 14702.5 14470.0 14267.5 13745.0 12967.5 9682.5 12505.0 5994.0 88139.0 19413.0 12287.0 1638.0 6444.0 7221.0 8944.0 30654.0 1380.0 30338.0 10057.0 4693.0 2440.0 4216.0 180735.0 53124.0 233859.0 7691 6107 13779 19703 9105 28808 7673 15170 12856 11083 5327 1560 3745 12430 15012 13962 17919 18106 19932 4889 10322 11431 15164 233859 Low High
7 2017 22050 2090 5140 265 950 1120 1640 655 910 2640 8000 15250 1710 4870 260 710 860 1200 590 630 2260 6650 608.700012 3103.300049 80.400002 5.3 76.099998 2494.699951 175.229996 243.526505 211.830994 214.937744 250.458755 179.497757 211.601257 282.455750 195.681747 127.823753 148.992752 310.851257 149.963501 158.511505 214.954742 77.326752 279.994263 241.283005 147.703995 228.611252 170.497757 130.923248 269.986237 214.666000 163.593002 159.660248 348.409241 390.851257 139.533249 182.877243 272.595490 207.358246 232.789993 166.677505 216.222748 3822.5 9607.5 12285.0 13912.5 14750.0 15095.0 14952.5 14665.0 14147.5 13327.5 9995.0 12832.5 6428.0 93624.0 20709.0 13820.0 1707.0 7011.0 7279.0 9358.0 32240.0 1450.0 31189.0 10745.0 5122.0 2604.0 4492.0 192176.0 55602.0 247778.0 11108 6438 14320 17562 9820 27381 7882 16763 12843 12294 5947 1830 4213 12263 15447 14487 19690 19297 21780 5330 10902 11924 15905 247778 Low High
8 2018 22660 2360 6720 290 860 970 1720 590 880 2940 7220 17380 2060 5530 217 730 1150 1560 485 770 2680 6270 600.799988 3165.199951 81.000000 4.8 77.099998 2564.300049 178.728500 283.244751 211.246750 258.770752 284.169250 210.156754 243.121506 327.308746 224.371002 139.305252 156.547256 319.922485 155.553253 189.209747 231.892502 86.007004 302.975006 271.968262 152.390747 249.154999 177.337997 140.409256 275.519501 234.194504 171.591003 166.481247 376.268005 453.763000 150.222244 187.268250 277.112244 222.082993 257.696014 175.389252 234.239502 4052.5 10072.5 12815.0 14397.5 15257.5 15665.0 15582.5 15247.5 14727.5 13915.0 10507.5 13357.5 6970.0 99794.0 22583.0 15106.0 1844.0 7567.0 8129.0 10123.0 33610.0 1632.0 33555.0 11754.0 5609.0 2798.0 4835.0 205727.0 60184.0 265911.0 12610 7422 15426 19546 10092 29638 8004 19009 13977 12896 6247 1922 4440 12909 15889 16262 20538 20772 23382 5728 11531 12275 16708 265911 Low High
9 2019 19170 2290 5700 270 900 980 1820 725 1010 3260 6840 20200 2130 4860 225 920 980 1630 690 920 2600 6690 608.700012 3219.500000 81.099998 4.5 77.500000 2610.899902 170.895752 290.044250 197.054245 286.093994 286.961487 212.753998 235.375000 340.894012 224.387756 148.065750 152.729004 310.461243 152.830002 194.334244 226.292252 91.447746 286.351746 268.006012 147.673248 282.870514 173.587006 141.817245 267.490753 244.368256 169.208496 154.995743 349.331238 473.024994 150.126999 187.576508 273.232513 209.394745 263.279999 176.359497 226.138748 4252.5 10460.0 13295.0 14860.0 15780.0 16160.0 16220.0 15822.5 15237.5 14417.5 11020.0 13852.5 7238.0 105433.0 24349.0 16322.0 1999.0 7886.0 8245.0 10744.0 34849.0 1685.0 35066.0 12484.0 5932.0 2966.0 5162.0 217065.0 63295.0 280360.0 12655 7652 16141 19888 10567 30455 8489 20314 14394 13503 6471 2105 4609 13609 16734 17161 21988 22127 25041 6116 12279 13099 18030 280360 Low High
10 2020 20070 1950 5130 250 940 990 1470 680 1090 2800 6460 17190 1740 4740 175 750 1010 1410 570 1000 2400 5170 622.200012 3272.399902 81.000000 4.3 77.500000 2650.300049 146.184753 271.098236 167.698502 316.912262 272.200256 218.504745 242.593246 294.454254 207.248245 139.345505 134.226257 288.206512 141.802505 193.320496 193.007751 77.951500 267.991241 261.999756 123.960999 272.392761 156.774246 128.820496 247.310501 216.382996 132.521744 130.047745 342.761261 454.219513 136.427994 165.027496 256.376495 181.031250 236.547256 154.667007 199.125748 4595.0 10627.5 13565.0 15062.5 15942.5 16417.5 16535.0 16147.5 15552.5 14747.5 11495.0 14167.5 7712.0 110312.0 25993.0 17283.0 2056.0 8397.0 8483.0 11344.0 37561.0 1725.0 37099.0 13097.0 6093.0 3135.0 5487.0 229139.0 66638.0 295777.0 13866 7383 16375 20524 10733 31257 8992 22400 14558 13983 6823 2140 4875 13817 17359 17594 23654 23810 27100 6289 13457 13795 19447 295777 Low Low
11 2021 22980 3300 6180 270 1390 1090 1780 790 1160 3800 7750 16450 2340 4950 185 770 930 1390 660 860 2800 5470 643.700012 3318.600098 80.599998 4.9 76.599998 2674.899902 189.515747 392.755005 248.804749 451.379486 381.414490 306.777740 344.608765 396.520996 291.072754 195.253494 177.007507 389.319000 197.629745 294.096985 233.626251 115.649002 401.498993 338.093994 157.812500 369.927002 191.849243 180.051498 329.804504 282.026245 194.417496 180.381500 487.175751 639.705994 183.621002 209.551743 336.203491 257.895996 337.497498 211.006256 282.832764 4875.0 11410.0 14522.5 16010.0 16915.0 17427.5 17552.5 17115.0 16440.0 15597.5 12175.0 15062.5 7804.0 113290.0 26954.0 18012.0 2256.0 8878.0 8077.0 12115.0 37606.0 1784.0 37011.0 12860.0 6180.0 3197.0 5582.0 234991.0 66613.0 301604.0 14319 6645 15200 19432 11376 30808 8555 23294 15958 15235 6002 1557 4255 11724 17244 18121 24686 25464 28022 5633 14219 14507 21356 301604 Low High
12 2022 23740 2460 5800 265 940 1130 1870 620 1160 3500 6770 17440 2070 4100 345 770 890 1510 570 1110 2720 5670 603.099976 3310.199951 81.800003 3.6 78.800003 2707.100098 200.328506 429.891235 286.851013 459.618011 394.096741 342.017487 402.063995 457.423737 302.150757 192.752747 188.985748 391.059265 213.925003 333.005493 338.508240 107.830002 437.896240 342.504486 173.082993 368.586761 188.493744 186.207748 348.890259 362.155762 208.888000 201.230743 492.319244 695.994019 185.337250 241.535507 342.635986 281.004242 386.713257 217.796249 314.244263 5285.0 12522.5 15717.5 17320.0 18235.0 18725.0 18897.5 18440.0 17642.5 16700.0 13062.5 16185.0 8684.0 124270.0 29378.0 19527.0 2318.0 9773.0 8911.0 13110.0 40777.0 1798.0 40380.0 14016.0 7144.0 3283.0 6038.0 256748.0 72659.0 329407.0 16373 6805 16463 19567 12030 31597 9658 24320 18992 16985 6851 1743 4896 13124 18604 20252 26033 27240 31774 6671 15185 15489 23665 329407 Low High

3.5.2 Create the Classified Column for GDP Auckland by 5 Grades.ΒΆ

InΒ [81]:
labels = ['E', 'D', 'C', 'B', 'A']

bins = [merged_data1['GDP_Auckland'].min(), 70000, 85000, 95000, 110000, merged_data1['GDP_Auckland'].max()]

merged_data1['GDP_Auckland_Grade'] = pd.cut(merged_data1['GDP_Auckland'], bins=bins, labels=labels, include_lowest=True)

merged_data1[['GDP_Auckland', 'GDP_Auckland_Grade']]
Out[81]:
GDP_Auckland GDP_Auckland_Grade
0 63382.0 E
1 65759.0 E
2 68978.0 E
3 71351.0 D
4 75444.0 D
5 81076.0 D
6 88139.0 C
7 93624.0 C
8 99794.0 B
9 105433.0 B
10 110312.0 A
11 113290.0 A
12 124270.0 A
InΒ [82]:
merged_data1
Out[82]:
Year job_creation_Auckland job_creation_Bay_of_Plenty job_creation_Canterbury job_creation_Gisborne job_creation_Hawkes_Bay job_creation_Manawatu_Wanganui job_creation_Otago job_creation_Taranaki job_creation_Tasman__Nelson__Marlborough__West_Coast job_creation_Waikato job_creation_Wellington job_destruction_Auckland job_destruction_Bay_of_Plenty job_destruction_Canterbury job_destruction_Gisborne job_destruction_Hawkes_Bay job_destruction_Manawatu_Wanganui job_destruction_Otago job_destruction_Taranaki job_destruction_Tasman__Nelson__Marlborough__West_Coast job_destruction_Waikato job_destruction_Wellington Not_in_Labour_Force Working_Age_Population Labour_Force_Participation_Rate Unemployment_Rate Employment_Rate Total_Labour_Force AVI_Auckland AVI_Bay_of_Plenty AVI_Canterbury AVI_Gisborne_Hawkes_Bay AVI_Marlborough_NelsonTasman_West_Coast AVI_Manawatu_Wanganui_Taranaki AVI_Northland AVI_Otago_Southland AVI_Waikato AVI_Wellington AVI_ids_Accounting AVI_ids_Construction AVI_ids_Education AVI_ids_Health AVI_ids_Hospitalty AVI_ids_IT AVI_ids_Manufacturing AVI_ids_Primary AVI_ids_Sales AVI_ids_Other AVI_ocp_Managers AVI_ocp_Professionals AVI_ocp_Tech_and_Trades AVI_ocp_Community_and_Personal_Services AVI_ocp_Clerical_and_Administration AVI_ocp_Sales AVI_ocp_Machinery_Drivers AVI_ocp_Labourers AVI_ocp_highly_skilled AVI_ocp_skilled AVI_ocp_semi_skilled AVI_ocp_low_skilled AVI_ocp_unskilled AVI_ocp_Skilled AVI_ocp_Unskilled median_wage_15_19 median_wage_20_24 median_wage_25_29 median_wage_30_34 median_wage_35_39 median_wage_40_44 median_wage_45_49 median_wage_50_54 median_wage_55_59 median_wage_60_64 median_wage_65 median_wage_Total_All_Ages GDP_Northland GDP_Auckland GDP_Waikato GDP_Bay_of_Plenty GDP_Gisborne GDP_Hawkes_Bay GDP_Taranaki GDP_Manawatu_Whanganui GDP_Wellington GDP_West_Coast GDP_Canterbury GDP_Otago GDP_Southland GDP_Marlborough GDP_Tasman_Nelson GDP_Total_North_Island GDP_Total_South_Island GDP_New_Zealand GDP_Agriculture GDP_Forestry__Fishing__and_Mining GDP_Forestry__Fishing__Mining__Electricity__Gas__Water_and_Waste_Services GDP_Primary_Manufacturing GDP_Other_Manufacturing GDP_Manufacturing GDP_Electricity__Gas__Water__and_Waste_services GDP_Construction GDP_Wholesale_Trade GDP_Retail_Trade GDP_Accommodation_and_Food_Services GDP_Accommodation GDP_Food_and_beverage_services GDP_Transport__Postal_and_Warehousing GDP_Information_Media__Telecommunications_and_Other_Services GDP_Financial_and_Insurance_Services GDP_Rental__Hiring_and_Real_Estate_Services GDP_Owner_Occupied_Property_Operation GDP_Professional__Scientific__and_Technical_Services GDP_Administrative_and_Support_Services GDP_Public_Administration_and_Safety GDP_Education_and_Training GDP_Health_Care_and_Social_Assistance GDP_Total_All_Industries Unemployment Rate Classified AVI Auckland Classified GDP_Auckland_Grade
0 2010 18130 1580 3320 250 830 840 1240 475 800 2730 4910 15380 1430 3470 195 850 980 1300 515 900 2270 5290 645.700012 2844.399902 77.300003 6.3 72.400002 2198.699951 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 3060.0 8050.0 10582.5 11865.0 12315.0 12137.5 12065.0 11905.0 11437.5 10515.0 7245.0 10632.5 4543.0 63382.0 15258.0 9409.0 1324.0 5150.0 7665.0 7544.0 25113.0 1351.0 22229.0 7894.0 4168.0 1794.0 3296.0 139388.0 40731.0 180119.0 8654 6651 12720 13001 7925 20926 6069 10464 9869 8292 3789 1226 2563 7929 12490 11119 13413 12192 14735 3633 8732 9204 11958 180119 High Low E
1 2011 17310 1600 4100 190 710 810 1140 480 850 2220 5310 14540 1370 3730 190 690 890 1110 490 760 1920 5120 655.799988 2866.699951 77.099998 6.4 72.199997 2211.000000 101.537247 103.784752 131.811493 107.877747 111.249252 104.018753 109.501999 116.044502 105.596497 101.322998 106.247749 115.751999 92.198753 104.268753 107.144249 102.214249 111.413254 123.854248 101.984749 108.947250 103.917252 106.695747 121.152000 115.340752 107.325996 105.281250 113.408501 118.313751 105.897751 103.693748 126.957001 111.033752 104.367249 108.664749 109.756752 3152.5 8162.5 10835.0 12155.0 12712.5 12565.0 12462.5 12290.0 11845.0 10967.5 7752.5 10972.5 4842.0 65759.0 16075.0 9947.0 1394.0 5447.0 7977.0 7888.0 25501.0 1427.0 23219.0 8171.0 4498.0 1813.0 3446.0 144830.0 42574.0 187404.0 10643 6938 13291 13860 8191 22051 6353 10470 10081 8469 3857 1248 2609 8758 12421 9975 13648 13484 15529 3959 8835 9595 12337 187404 High Low E
2 2012 17260 1560 4580 195 820 1040 1230 510 800 2710 5880 14410 1510 3710 160 920 940 1200 450 790 2290 5080 652.500000 2876.300049 77.300003 6.3 72.400002 2223.800049 104.071747 110.633003 170.327499 120.652748 129.169006 117.805748 115.172501 138.578751 110.881500 102.747253 109.499001 147.468002 83.279999 119.278000 112.457253 97.901749 125.172997 148.839752 105.112251 113.703247 106.715752 107.819504 142.382004 133.338745 113.677498 107.552750 137.171494 145.013245 107.701500 110.109497 150.747253 120.932503 116.904999 113.887749 120.466751 3257.5 8370.0 11112.5 12452.5 13102.5 13020.0 12925.0 12735.0 12280.0 11435.0 8245.0 11335.0 4922.0 68978.0 17102.0 10321.0 1432.0 5575.0 7896.0 7997.0 26724.0 1517.0 24316.0 8402.0 4599.0 1919.0 3503.0 150947.0 44256.0 195203.0 10622 6772 13371 15298 8017 23315 6600 10804 10526 8878 3940 1245 2696 9331 12960 10625 14757 14344 15869 4172 9041 9985 12665 195203 High Low E
3 2013 18320 1800 4360 305 790 770 1290 660 830 2110 5820 13340 1470 3770 245 710 780 1070 480 760 1950 4720 666.900024 2878.800049 76.800003 6.6 71.800003 2211.899902 116.276497 130.563995 191.616257 127.156250 136.180756 137.263504 120.304001 150.940750 123.468246 106.194748 112.277748 184.800247 92.263496 123.594498 134.522247 92.378502 153.481247 158.919006 113.936996 122.010750 116.591751 108.565002 171.246750 157.548996 120.545998 118.168999 180.684998 190.806747 110.852997 123.290001 181.366257 137.462250 138.647995 123.331497 138.688507 3290.0 8555.0 11340.0 12705.0 13407.5 13420.0 13267.5 13065.0 12647.5 11850.0 8620.0 11617.5 4899.0 71351.0 16725.0 10490.0 1427.0 5730.0 7967.0 7999.0 27149.0 1449.0 25371.0 8526.0 4432.0 2001.0 3581.0 153739.0 45360.0 199099.0 9130 6610 13764 15133 7938 23071 7154 11541 10530 9489 4124 1270 2855 9790 13188 10921 15134 15260 16555 4165 9147 10154 13133 199099 High Low D
4 2014 18870 1580 4710 285 690 860 1380 550 1010 2350 6050 15940 1620 3600 245 720 800 1180 600 690 2400 4680 641.799988 2899.399902 77.900002 6.0 73.199997 2257.600098 133.691757 154.134003 220.149506 143.167496 165.084747 144.570755 141.191498 188.934753 140.002243 111.706253 121.781998 230.177505 106.677002 129.583496 162.815247 95.911003 181.682999 183.427505 128.405243 121.794746 135.623993 115.741753 207.521744 180.007996 132.238754 131.477249 223.298996 249.287994 120.819504 142.899002 209.124252 158.863007 167.512497 138.882507 162.422501 3392.5 8820.0 11590.0 13025.0 13767.5 13877.5 13655.0 13475.0 13017.5 12245.0 9032.5 11925.0 5363.0 75444.0 18690.0 11030.0 1474.0 6044.0 8508.0 8558.0 28348.0 1548.0 28198.0 9224.0 5075.0 2224.0 3873.0 163457.0 50143.0 213600.0 13111 6842 14266 15619 8209 23828 7424 12398 11592 9987 4368 1336 3034 10356 13731 12265 16128 16147 17763 4283 9399 10503 13473 213600 High Low D
5 2015 18550 1920 4540 180 780 720 1340 485 770 2440 5750 16200 1390 3960 205 800 780 1240 550 970 2510 4920 625.299988 2953.699951 78.800003 5.6 74.400002 2328.300049 145.303253 172.999496 209.960754 155.774750 165.059494 143.589996 150.659256 198.530746 137.801743 107.152000 132.479752 241.839005 129.024750 136.664001 171.364746 84.667503 188.766251 170.084000 129.656998 125.584000 142.624496 117.330002 215.301498 185.268997 139.214996 135.110001 226.864502 258.147491 122.263000 153.565750 220.054993 163.960999 173.757248 142.871506 167.606003 3470.0 9025.0 11745.0 13300.0 14057.5 14255.0 14037.5 13852.5 13335.0 12595.0 9360.0 12192.5 5555.0 81076.0 18754.0 11291.0 1541.0 6176.0 8329.0 8681.0 29588.0 1479.0 29434.0 9427.0 4841.0 2338.0 3996.0 170990.0 51516.0 222506.0 8240 6936 14531 18113 8753 26866 7595 13833 12486 10491 4763 1364 3420 11424 14452 13514 16526 17148 18570 4556 9824 10998 14263 222506 Low Low D
6 2016 18440 2120 5510 285 730 1000 1560 585 830 2820 5990 14920 2220 4270 285 700 800 1270 600 880 2130 5350 646.799988 3022.500000 78.599998 5.6 74.199997 2375.800049 165.826004 216.454498 201.011993 185.867996 197.029999 155.698746 170.623505 227.022003 159.732498 118.360252 147.285751 278.177765 139.579498 144.005997 196.753754 84.843498 219.529999 196.574997 141.570251 154.535995 158.963257 124.945999 250.199753 201.402496 156.169006 144.600494 269.366760 312.987000 131.723007 172.766006 252.503250 181.384995 202.331497 157.087494 188.636505 3570.0 9267.5 11977.5 13605.0 14405.0 14702.5 14470.0 14267.5 13745.0 12967.5 9682.5 12505.0 5994.0 88139.0 19413.0 12287.0 1638.0 6444.0 7221.0 8944.0 30654.0 1380.0 30338.0 10057.0 4693.0 2440.0 4216.0 180735.0 53124.0 233859.0 7691 6107 13779 19703 9105 28808 7673 15170 12856 11083 5327 1560 3745 12430 15012 13962 17919 18106 19932 4889 10322 11431 15164 233859 Low High C
7 2017 22050 2090 5140 265 950 1120 1640 655 910 2640 8000 15250 1710 4870 260 710 860 1200 590 630 2260 6650 608.700012 3103.300049 80.400002 5.3 76.099998 2494.699951 175.229996 243.526505 211.830994 214.937744 250.458755 179.497757 211.601257 282.455750 195.681747 127.823753 148.992752 310.851257 149.963501 158.511505 214.954742 77.326752 279.994263 241.283005 147.703995 228.611252 170.497757 130.923248 269.986237 214.666000 163.593002 159.660248 348.409241 390.851257 139.533249 182.877243 272.595490 207.358246 232.789993 166.677505 216.222748 3822.5 9607.5 12285.0 13912.5 14750.0 15095.0 14952.5 14665.0 14147.5 13327.5 9995.0 12832.5 6428.0 93624.0 20709.0 13820.0 1707.0 7011.0 7279.0 9358.0 32240.0 1450.0 31189.0 10745.0 5122.0 2604.0 4492.0 192176.0 55602.0 247778.0 11108 6438 14320 17562 9820 27381 7882 16763 12843 12294 5947 1830 4213 12263 15447 14487 19690 19297 21780 5330 10902 11924 15905 247778 Low High C
8 2018 22660 2360 6720 290 860 970 1720 590 880 2940 7220 17380 2060 5530 217 730 1150 1560 485 770 2680 6270 600.799988 3165.199951 81.000000 4.8 77.099998 2564.300049 178.728500 283.244751 211.246750 258.770752 284.169250 210.156754 243.121506 327.308746 224.371002 139.305252 156.547256 319.922485 155.553253 189.209747 231.892502 86.007004 302.975006 271.968262 152.390747 249.154999 177.337997 140.409256 275.519501 234.194504 171.591003 166.481247 376.268005 453.763000 150.222244 187.268250 277.112244 222.082993 257.696014 175.389252 234.239502 4052.5 10072.5 12815.0 14397.5 15257.5 15665.0 15582.5 15247.5 14727.5 13915.0 10507.5 13357.5 6970.0 99794.0 22583.0 15106.0 1844.0 7567.0 8129.0 10123.0 33610.0 1632.0 33555.0 11754.0 5609.0 2798.0 4835.0 205727.0 60184.0 265911.0 12610 7422 15426 19546 10092 29638 8004 19009 13977 12896 6247 1922 4440 12909 15889 16262 20538 20772 23382 5728 11531 12275 16708 265911 Low High B
9 2019 19170 2290 5700 270 900 980 1820 725 1010 3260 6840 20200 2130 4860 225 920 980 1630 690 920 2600 6690 608.700012 3219.500000 81.099998 4.5 77.500000 2610.899902 170.895752 290.044250 197.054245 286.093994 286.961487 212.753998 235.375000 340.894012 224.387756 148.065750 152.729004 310.461243 152.830002 194.334244 226.292252 91.447746 286.351746 268.006012 147.673248 282.870514 173.587006 141.817245 267.490753 244.368256 169.208496 154.995743 349.331238 473.024994 150.126999 187.576508 273.232513 209.394745 263.279999 176.359497 226.138748 4252.5 10460.0 13295.0 14860.0 15780.0 16160.0 16220.0 15822.5 15237.5 14417.5 11020.0 13852.5 7238.0 105433.0 24349.0 16322.0 1999.0 7886.0 8245.0 10744.0 34849.0 1685.0 35066.0 12484.0 5932.0 2966.0 5162.0 217065.0 63295.0 280360.0 12655 7652 16141 19888 10567 30455 8489 20314 14394 13503 6471 2105 4609 13609 16734 17161 21988 22127 25041 6116 12279 13099 18030 280360 Low High B
10 2020 20070 1950 5130 250 940 990 1470 680 1090 2800 6460 17190 1740 4740 175 750 1010 1410 570 1000 2400 5170 622.200012 3272.399902 81.000000 4.3 77.500000 2650.300049 146.184753 271.098236 167.698502 316.912262 272.200256 218.504745 242.593246 294.454254 207.248245 139.345505 134.226257 288.206512 141.802505 193.320496 193.007751 77.951500 267.991241 261.999756 123.960999 272.392761 156.774246 128.820496 247.310501 216.382996 132.521744 130.047745 342.761261 454.219513 136.427994 165.027496 256.376495 181.031250 236.547256 154.667007 199.125748 4595.0 10627.5 13565.0 15062.5 15942.5 16417.5 16535.0 16147.5 15552.5 14747.5 11495.0 14167.5 7712.0 110312.0 25993.0 17283.0 2056.0 8397.0 8483.0 11344.0 37561.0 1725.0 37099.0 13097.0 6093.0 3135.0 5487.0 229139.0 66638.0 295777.0 13866 7383 16375 20524 10733 31257 8992 22400 14558 13983 6823 2140 4875 13817 17359 17594 23654 23810 27100 6289 13457 13795 19447 295777 Low Low A
11 2021 22980 3300 6180 270 1390 1090 1780 790 1160 3800 7750 16450 2340 4950 185 770 930 1390 660 860 2800 5470 643.700012 3318.600098 80.599998 4.9 76.599998 2674.899902 189.515747 392.755005 248.804749 451.379486 381.414490 306.777740 344.608765 396.520996 291.072754 195.253494 177.007507 389.319000 197.629745 294.096985 233.626251 115.649002 401.498993 338.093994 157.812500 369.927002 191.849243 180.051498 329.804504 282.026245 194.417496 180.381500 487.175751 639.705994 183.621002 209.551743 336.203491 257.895996 337.497498 211.006256 282.832764 4875.0 11410.0 14522.5 16010.0 16915.0 17427.5 17552.5 17115.0 16440.0 15597.5 12175.0 15062.5 7804.0 113290.0 26954.0 18012.0 2256.0 8878.0 8077.0 12115.0 37606.0 1784.0 37011.0 12860.0 6180.0 3197.0 5582.0 234991.0 66613.0 301604.0 14319 6645 15200 19432 11376 30808 8555 23294 15958 15235 6002 1557 4255 11724 17244 18121 24686 25464 28022 5633 14219 14507 21356 301604 Low High A
12 2022 23740 2460 5800 265 940 1130 1870 620 1160 3500 6770 17440 2070 4100 345 770 890 1510 570 1110 2720 5670 603.099976 3310.199951 81.800003 3.6 78.800003 2707.100098 200.328506 429.891235 286.851013 459.618011 394.096741 342.017487 402.063995 457.423737 302.150757 192.752747 188.985748 391.059265 213.925003 333.005493 338.508240 107.830002 437.896240 342.504486 173.082993 368.586761 188.493744 186.207748 348.890259 362.155762 208.888000 201.230743 492.319244 695.994019 185.337250 241.535507 342.635986 281.004242 386.713257 217.796249 314.244263 5285.0 12522.5 15717.5 17320.0 18235.0 18725.0 18897.5 18440.0 17642.5 16700.0 13062.5 16185.0 8684.0 124270.0 29378.0 19527.0 2318.0 9773.0 8911.0 13110.0 40777.0 1798.0 40380.0 14016.0 7144.0 3283.0 6038.0 256748.0 72659.0 329407.0 16373 6805 16463 19567 12030 31597 9658 24320 18992 16985 6851 1743 4896 13124 18604 20252 26033 27240 31774 6671 15185 15489 23665 329407 Low High A

3.5.3 Display the Correlation of Overall Unemployment Rate with Auckland's AVI and GDP by Pivot Table.ΒΆ

InΒ [83]:
merged_data1.pivot_table(values='AVI_Auckland',index=['GDP_Auckland_Grade', 'AVI Auckland Classified'], aggfunc='mean' )
Out[83]:
AVI_Auckland
GDP_Auckland_Grade AVI Auckland Classified
E Low 101.869667
D Low 131.757156
C High 170.528000
B High 174.812134
A High 194.922119
Low 146.184753

When categorizing Auckland's GDP levels from the highest (A) to the lowest (E) and dividing AVI Auckland by the median, we observe an interesting pattern: a high GDP does not necessarily correspond to a low AVI (indicating fewer job vacancies). This suggests that high GDP industries might not be directly related to labor-intensive support, meaning they could generate substantial economic output without a proportional increase in job vacancies.

InΒ [84]:
merged_data1.pivot_table(values=['AVI_Auckland'], index=['Unemployment Rate Classified','GDP_Auckland_Grade'], columns='AVI Auckland Classified')
Out[84]:
AVI_Auckland
AVI Auckland Classified High Low
Unemployment Rate Classified GDP_Auckland_Grade
High E NaN 101.869667
D NaN 124.984131
Low D NaN 145.303253
C 170.528000 NaN
B 174.812134 NaN
A 194.922119 146.184753

Including the overall unemployment rate of New Zealand in the analysis, we observe that higher unemployment rates correlate with lower GDP for Auckland. This matchs with our general understanding that higher unemployment negatively impacts economic output.

InΒ [85]:
merged_data1.pivot_table(values='AVI_Auckland', index=['Unemployment Rate Classified','GDP_Auckland_Grade'], columns='AVI Auckland Classified', aggfunc=len, margins=True)
Out[85]:
AVI Auckland Classified High Low All
Unemployment Rate Classified GDP_Auckland_Grade
High E NaN 3.0 3
D NaN 2.0 2
Low D NaN 1.0 1
C 2.0 NaN 2
B 2.0 NaN 2
A 2.0 1.0 3
All 6.0 7.0 13

Counting the number of entries in each category, we find a total of 13 observations. Among these, 6 have high AVI and 7 have low AVI. Additionally, the distribution of GDP Auckland across each grade is clearly visible.

InΒ [86]:
crosstab_AVI_Auckland = pd.crosstab(index=[merged_data1['Unemployment Rate Classified'], merged_data1['GDP_Auckland_Grade']],
    columns=merged_data1['AVI Auckland Classified'],
    values=merged_data1['AVI_Auckland'],
    aggfunc='count',
    margins=True)
crosstab_AVI_Auckland
Out[86]:
AVI Auckland Classified High Low All
Unemployment Rate Classified GDP_Auckland_Grade
High E 0 3 3.0
D 0 2 2.0
C 0 0 NaN
B 0 0 NaN
A 0 0 NaN
Low E 0 0 NaN
D 0 1 1.0
C 2 0 2.0
B 2 0 2.0
A 2 1 3.0
All 6 7 13.0

By presenting the same content using a cross table, we can see that the crosstabulation expands all data, displaying the frequency distribution of variables in a matrix format.

3.5.4 Visualize the Correlation of Overall Unemployment Rate with Auckland's AVI and GDP.ΒΆ

InΒ [87]:
Unemployment_AVIAuckland = merged_data1.groupby('Unemployment Rate Classified')['GDP_Auckland'].sum()

Unemployment_AVIAuckland.plot(kind='bar', figsize=(10, 6), color='Orange', edgecolor='black')
plt.title('Unemployment Rate in New Zealand Relates to GDP Auckland')
plt.xlabel('Unemployment Rate Classified')
plt.ylabel('GDP Auckland')
Out[87]:
Text(0, 0.5, 'GDP Auckland')
No description has been provided for this image

Based on the chart, we can see that when summing the GDP of Auckland for both high and low unemployment rates, the GDP is significantly lower for the group with high unemployment rates. Conversely, the GDP is higher for the group with low unemployment rates. This implies that high unemployment rates in New Zealand lead to lower GDP in Auckland, and vice versa.

InΒ [88]:
Unemployment_AVIAuckland = merged_data1.groupby('Unemployment Rate Classified')['AVI_Auckland'].sum()

Unemployment_AVIAuckland.plot(kind='bar', figsize=(10, 6), color='blue', edgecolor='black')
plt.title('Unemployment Rate in New Zealand Relates to AVI Auckland')
plt.xlabel('Unemployment Rate Classified')
plt.ylabel('AVI Auckland')
Out[88]:
Text(0, 0.5, 'AVI Auckland')
No description has been provided for this image

This table similarly categorizes data by New Zealand's unemployment rates. High unemployment rates lead to low AVI, while low unemployment rates lead to high AVI. This implies that lower unemployment rates correlate with more job opportunities, or conversely, that more job opportunities (high AVI) can result in lower unemployment rates.

3.6 Does the Job Creation in Auckland from 1999 to 2022 Remain Positive, Making Auckland the City with the Most Economic Contribution?ΒΆ

3.6.1 Build a pivot table that classifies job creation and job destruction in Auckland from 1999 to 2022,ΒΆ

InΒ [89]:
regionjob
Out[89]:
Date Type Auckland Waikato Bay of Plenty Gisborne Hawke's Bay Taranaki Manawatu-Wanganui Wellington Tasman, Nelson, Marlborough, West Coast Canterbury Otago Year
1 1999Q3 Job Creation 2710 340 370 35 120 90 170 1080 130 670 170 1999
2 1999Q4 Job Creation 3040 500 260 30 150 120 220 1100 100 820 220 1999
3 2000Q1 Job Creation 3340 610 420 45 210 210 240 1530 130 750 210 2000
4 2000Q2 Job Creation 3700 760 400 25 210 180 260 1820 150 1140 380 2000
5 2000Q3 Job Creation 3340 430 320 40 180 120 250 1530 130 690 200 2000
6 2000Q4 Job Creation 3060 410 280 40 280 120 220 1170 130 710 190 2000
7 2001Q1 Job Creation 4760 480 420 75 230 160 230 2870 150 2180 270 2001
8 2001Q2 Job Creation 4000 640 350 30 150 140 230 1620 190 890 300 2001
9 2001Q3 Job Creation 3280 530 290 30 160 140 220 1200 120 790 230 2001
10 2001Q4 Job Creation 3060 540 290 30 270 160 170 1130 130 720 200 2001
11 2002Q1 Job Creation 4420 610 280 65 400 190 260 2280 180 750 200 2002
12 2002Q2 Job Creation 3130 510 780 45 390 160 180 1130 150 730 200 2002
13 2002Q3 Job Creation 3680 520 310 45 140 150 200 1390 150 730 260 2002
14 2002Q4 Job Creation 3490 440 310 35 240 120 210 1010 140 730 180 2002
15 2003Q1 Job Creation 2610 320 290 35 250 210 240 840 160 670 300 2003
16 2003Q2 Job Creation 5190 490 1020 45 160 150 270 1790 200 900 280 2003
17 2003Q3 Job Creation 3920 700 300 30 200 210 310 1300 190 860 290 2003
18 2003Q4 Job Creation 2870 710 330 40 210 140 220 900 160 840 270 2003
19 2004Q1 Job Creation 3010 460 410 40 290 120 230 1140 180 790 340 2004
20 2004Q2 Job Creation 4860 990 560 55 200 270 280 1560 190 1010 280 2004
21 2004Q3 Job Creation 3290 580 330 20 210 150 190 1230 140 790 260 2004
22 2004Q4 Job Creation 3370 690 330 50 130 160 260 1060 190 820 280 2004
23 2005Q1 Job Creation 3700 580 360 85 240 190 380 1590 180 940 260 2005
24 2005Q2 Job Creation 4200 620 580 45 280 180 270 1440 170 1170 310 2005
25 2005Q3 Job Creation 3880 500 390 40 140 110 280 1140 190 900 260 2005
26 2005Q4 Job Creation 3810 490 390 30 210 170 250 1350 260 890 210 2005
27 2006Q1 Job Creation 3920 480 330 50 210 170 230 1630 220 1000 390 2006
28 2006Q2 Job Creation 5060 840 640 50 430 190 240 1620 290 1090 390 2006
29 2006Q3 Job Creation 4030 650 400 70 170 120 240 1360 190 970 410 2006
30 2006Q4 Job Creation 3420 570 370 45 180 100 190 1380 200 870 270 2006
31 2007Q1 Job Creation 3700 490 380 45 190 120 270 1210 190 930 280 2007
32 2007Q2 Job Creation 4960 630 670 90 230 160 240 1850 250 1380 400 2007
33 2007Q3 Job Creation 3980 820 420 85 200 170 240 1390 220 950 330 2007
34 2007Q4 Job Creation 4740 880 400 75 220 170 210 1510 310 940 310 2007
35 2008Q1 Job Creation 3890 600 400 60 310 160 220 1400 210 1080 430 2008
36 2008Q2 Job Creation 5430 750 690 45 300 230 610 1920 350 1340 490 2008
37 2008Q3 Job Creation 3260 480 480 50 150 220 210 1270 200 840 260 2008
38 2008Q4 Job Creation 3360 680 380 50 220 140 200 1130 250 940 300 2008
39 2009Q1 Job Creation 3360 500 320 65 220 150 310 1320 360 840 340 2009
40 2009Q2 Job Creation 3820 690 790 30 150 190 240 2420 240 1250 310 2009
41 2009Q3 Job Creation 3150 440 240 30 140 110 160 890 160 750 230 2009
42 2009Q4 Job Creation 3060 470 310 45 210 100 180 980 210 760 230 2009
43 2010Q1 Job Creation 4730 610 330 60 180 150 220 1100 170 660 290 2010
44 2010Q2 Job Creation 4920 830 620 50 270 140 230 1650 250 1010 370 2010
45 2010Q3 Job Creation 5030 610 330 30 160 85 230 1100 190 890 290 2010
46 2010Q4 Job Creation 3450 680 300 110 220 100 160 1060 190 760 290 2010
47 2011Q1 Job Creation 5480 440 460 55 190 110 230 1440 220 1110 290 2011
48 2011Q2 Job Creation 4910 640 560 55 190 110 230 1590 230 1030 340 2011
49 2011Q3 Job Creation 3590 470 300 35 150 110 150 1080 180 880 240 2011
50 2011Q4 Job Creation 3330 670 280 45 180 150 200 1200 220 1080 270 2011
51 2012Q1 Job Creation 5090 790 250 35 230 130 290 1600 230 1280 300 2012
52 2012Q2 Job Creation 4700 870 590 55 240 110 320 1700 200 1140 310 2012
53 2012Q3 Job Creation 3090 440 280 45 140 150 210 1110 160 950 280 2012
54 2012Q4 Job Creation 4380 610 440 60 210 120 220 1470 210 1210 340 2012
55 2013Q1 Job Creation 4250 460 290 140 160 120 150 1150 190 1010 260 2013
56 2013Q2 Job Creation 6240 550 620 75 260 200 250 1680 270 1240 390 2013
57 2013Q3 Job Creation 3970 500 430 40 160 170 190 1440 180 1020 330 2013
58 2013Q4 Job Creation 3860 600 460 50 210 170 180 1550 190 1090 310 2013
59 2014Q1 Job Creation 4020 560 340 130 180 110 210 1670 340 1380 340 2014
60 2014Q2 Job Creation 6400 700 550 55 230 220 260 1660 270 1200 400 2014
61 2014Q3 Job Creation 4040 460 320 45 130 130 200 1320 180 1010 330 2014
62 2014Q4 Job Creation 4410 630 370 55 150 90 190 1400 220 1120 310 2014
63 2015Q1 Job Creation 4690 510 330 75 150 95 180 1430 210 1100 370 2015
64 2015Q2 Job Creation 4910 730 690 25 240 160 170 1590 210 1430 350 2015
65 2015Q3 Job Creation 4700 540 410 40 170 100 210 1550 170 960 320 2015
66 2015Q4 Job Creation 4250 660 490 40 220 130 160 1180 180 1050 300 2015
67 2016Q1 Job Creation 4450 710 460 120 160 160 210 1530 190 1480 330 2016
68 2016Q2 Job Creation 5090 730 750 75 230 190 270 1760 240 1500 630 2016
69 2016Q3 Job Creation 4190 560 470 40 160 150 210 1270 180 1370 310 2016
70 2016Q4 Job Creation 4710 820 440 50 180 85 310 1430 220 1160 290 2016
71 2017Q1 Job Creation 5210 580 450 85 250 85 390 1780 270 1320 460 2017
72 2017Q2 Job Creation 7330 900 790 70 260 210 290 2160 240 1420 450 2017
73 2017Q3 Job Creation 4600 490 390 70 190 200 220 1930 210 1040 390 2017
74 2017Q4 Job Creation 4910 670 460 40 250 160 220 2130 190 1360 340 2017
75 2018Q1 Job Creation 5630 890 530 120 220 120 220 2150 180 1770 450 2018
76 2018Q2 Job Creation 5750 900 890 65 220 190 260 2070 260 2180 600 2018
77 2018Q3 Job Creation 4200 570 450 45 190 140 260 1430 170 1430 320 2018
78 2018Q4 Job Creation 7080 580 490 60 230 140 230 1570 270 1340 350 2018
79 2019Q1 Job Creation 4760 910 810 90 280 220 280 1610 270 1410 680 2019
80 2019Q2 Job Creation 5840 880 690 80 260 210 260 1840 280 1740 360 2019
81 2019Q3 Job Creation 4220 630 420 50 170 200 250 1510 190 1330 380 2019
82 2019Q4 Job Creation 4350 840 370 50 190 95 190 1880 270 1220 400 2019
83 2020Q1 Job Creation 7360 900 650 70 370 220 300 2280 340 2000 510 2020
84 2020Q2 Job Creation 5110 680 500 50 170 220 190 1500 230 1160 290 2020
85 2020Q3 Job Creation 3520 600 380 65 170 110 260 1250 250 880 270 2020
86 2020Q4 Job Creation 4080 620 420 65 230 130 240 1430 270 1090 400 2020
87 2021Q1 Job Creation 4160 870 820 55 250 150 220 1450 300 1360 430 2021
88 2021Q2 Job Creation 7920 1290 1330 80 640 250 340 2690 330 1730 490 2021
89 2021Q3 Job Creation 5390 680 410 50 220 190 290 1610 220 1420 380 2021
90 2021Q4 Job Creation 5510 960 740 85 280 200 240 2000 310 1670 480 2021
91 2022Q1 Job Creation 5480 750 620 80 240 130 240 1630 300 1600 460 2022
92 2022Q2 Job Creation 7750 1300 820 70 290 180 410 1990 330 1690 550 2022
93 2022Q3 Job Creation 4830 720 410 60 200 160 220 1550 260 1200 420 2022
94 2022Q4 Job Creation 5680 730 610 55 210 150 260 1600 270 1310 440 2022
96 1999Q3 Job Destruction 2510 330 220 35 190 75 150 1100 95 530 430 1999
97 1999Q4 Job Destruction 2000 390 210 25 85 70 150 1030 70 460 140 1999
98 2000Q1 Job Destruction 5060 580 290 40 210 140 250 1880 160 970 400 2000
99 2000Q2 Job Destruction 2680 410 360 50 200 200 240 1340 130 790 240 2000
100 2000Q3 Job Destruction 2600 470 230 30 210 110 180 1110 110 740 280 2000
101 2000Q4 Job Destruction 2860 340 250 25 130 90 260 1020 110 540 150 2000
102 2001Q1 Job Destruction 3820 420 250 25 180 120 330 1570 170 930 270 2001
103 2001Q2 Job Destruction 3680 410 210 50 250 85 310 2350 140 1720 180 2001
104 2001Q3 Job Destruction 2660 300 220 20 190 95 170 1290 90 720 250 2001
105 2001Q4 Job Destruction 2250 330 260 25 110 150 170 970 110 640 170 2001
106 2002Q1 Job Destruction 4690 510 430 40 140 210 180 2100 120 1000 230 2002
107 2002Q2 Job Destruction 2770 430 310 50 260 110 210 1700 170 780 210 2002
108 2002Q3 Job Destruction 3080 390 600 45 360 100 170 1290 110 680 180 2002
109 2002Q4 Job Destruction 2220 310 320 30 130 60 150 880 100 500 170 2002
110 2003Q1 Job Destruction 3340 350 280 45 230 85 220 1460 130 630 190 2003
111 2003Q2 Job Destruction 3290 380 270 50 220 160 220 1620 150 750 180 2003
112 2003Q3 Job Destruction 2720 350 690 30 240 75 180 940 120 580 160 2003
113 2003Q4 Job Destruction 2960 340 440 20 190 110 150 990 130 520 210 2003
114 2004Q1 Job Destruction 3130 540 370 50 140 140 210 1060 140 730 250 2004
115 2004Q2 Job Destruction 3470 460 340 40 190 140 220 1570 170 600 240 2004
116 2004Q3 Job Destruction 2700 310 330 30 210 130 180 910 100 600 210 2004
117 2004Q4 Job Destruction 2390 420 240 30 120 170 170 830 95 500 170 2004
118 2005Q1 Job Destruction 3330 530 470 45 150 130 250 1050 140 840 240 2005
119 2005Q2 Job Destruction 3740 520 360 65 180 190 240 1180 180 800 240 2005
120 2005Q3 Job Destruction 2580 440 420 25 310 160 260 1240 130 800 190 2005
121 2005Q4 Job Destruction 2440 360 240 40 130 75 180 780 130 560 270 2005
122 2006Q1 Job Destruction 4060 700 350 60 220 190 280 1600 170 920 250 2006
123 2006Q2 Job Destruction 4770 520 330 60 230 120 580 1230 180 1300 310 2006
124 2006Q3 Job Destruction 2620 550 400 25 190 150 170 1030 170 660 180 2006
125 2006Q4 Job Destruction 3300 510 300 35 160 150 480 1050 140 760 320 2006
126 2007Q1 Job Destruction 4240 670 370 70 200 110 250 1440 200 930 330 2007
127 2007Q2 Job Destruction 3490 480 410 30 200 150 300 1440 270 910 220 2007
128 2007Q3 Job Destruction 3440 380 460 35 210 170 180 1150 150 860 250 2007
129 2007Q4 Job Destruction 2950 370 360 80 110 120 180 880 160 760 220 2007
130 2008Q1 Job Destruction 4150 720 380 65 240 110 290 1670 290 960 540 2008
131 2008Q2 Job Destruction 3570 460 490 65 240 170 290 1360 240 1070 350 2008
132 2008Q3 Job Destruction 3510 510 730 25 260 110 200 1050 200 810 280 2008
133 2008Q4 Job Destruction 3600 510 340 45 150 150 260 1120 160 730 250 2008
134 2009Q1 Job Destruction 5070 1010 740 55 160 150 250 1670 220 1150 590 2009
135 2009Q2 Job Destruction 5000 610 310 85 250 150 220 1280 350 910 280 2009
136 2009Q3 Job Destruction 3840 510 590 40 200 140 250 1210 180 810 310 2009
137 2009Q4 Job Destruction 3600 360 260 35 120 120 160 910 150 630 200 2009
138 2010Q1 Job Destruction 4290 680 350 45 180 190 260 1580 310 970 380 2010
139 2010Q2 Job Destruction 4960 570 320 65 190 130 380 1860 210 1140 310 2010
140 2010Q3 Job Destruction 3360 590 520 50 320 120 180 1040 180 740 390 2010
141 2010Q4 Job Destruction 2770 430 240 35 160 75 160 810 200 620 220 2010
142 2011Q1 Job Destruction 4440 710 400 65 150 110 290 1400 180 1040 320 2011
143 2011Q2 Job Destruction 3250 520 290 35 250 170 220 1590 240 1250 210 2011
144 2011Q3 Job Destruction 4130 340 450 40 160 140 150 1120 150 750 290 2011
145 2011Q4 Job Destruction 2720 350 230 50 130 70 230 1010 190 690 290 2011
146 2012Q1 Job Destruction 4430 930 380 60 230 160 340 1470 190 1160 280 2012
147 2012Q2 Job Destruction 3920 500 270 45 230 100 230 980 200 1130 270 2012
148 2012Q3 Job Destruction 2880 350 480 20 270 90 180 990 240 720 380 2012
149 2012Q4 Job Destruction 3180 510 380 35 190 100 190 1640 160 700 270 2012
150 2013Q1 Job Destruction 3790 600 430 90 160 120 210 1240 260 1180 290 2013
151 2013Q2 Job Destruction 3780 480 280 100 140 100 180 990 180 900 260 2013
152 2013Q3 Job Destruction 3140 460 440 30 240 110 220 1380 150 950 220 2013
153 2013Q4 Job Destruction 2630 410 320 25 170 150 170 1110 170 740 300 2013
154 2014Q1 Job Destruction 5840 1030 540 65 200 140 210 1660 190 1040 400 2014
155 2014Q2 Job Destruction 3510 550 360 110 180 120 260 1080 190 910 300 2014
156 2014Q3 Job Destruction 3260 420 460 35 180 140 170 1060 180 870 220 2014
157 2014Q4 Job Destruction 3330 400 260 35 160 200 160 880 130 780 260 2014
158 2015Q1 Job Destruction 4380 960 300 35 220 120 240 1500 320 1190 310 2015
159 2015Q2 Job Destruction 5150 540 340 70 140 150 210 1180 210 1120 340 2015
160 2015Q3 Job Destruction 3850 500 470 70 260 160 160 1190 290 950 300 2015
161 2015Q4 Job Destruction 2820 510 280 30 180 120 170 1050 150 700 290 2015
162 2016Q1 Job Destruction 4130 780 410 80 260 140 230 1530 330 1270 500 2016
163 2016Q2 Job Destruction 4090 520 300 130 130 180 210 1680 200 1030 250 2016
164 2016Q3 Job Destruction 3520 440 640 45 180 180 180 1100 180 930 250 2016
165 2016Q4 Job Destruction 3180 390 870 30 130 100 180 1040 170 1040 270 2016
166 2017Q1 Job Destruction 3930 740 380 60 160 130 230 1950 180 2280 300 2017
167 2017Q2 Job Destruction 4100 620 460 95 180 190 340 1550 160 1040 380 2017
168 2017Q3 Job Destruction 3930 510 600 55 190 160 140 1560 160 860 230 2017
169 2017Q4 Job Destruction 3290 390 270 50 180 110 150 1590 130 690 290 2017
170 2018Q1 Job Destruction 4470 860 510 55 210 110 390 2120 240 1840 440 2018
171 2018Q2 Job Destruction 4610 770 470 120 170 95 320 1570 180 1310 410 2018
172 2018Q3 Job Destruction 4720 550 720 12 200 120 220 1480 210 1390 360 2018
173 2018Q4 Job Destruction 3580 500 360 30 150 160 220 1100 140 990 350 2018
174 2019Q1 Job Destruction 5350 620 570 55 220 130 240 1770 270 1290 390 2019
175 2019Q2 Job Destruction 4690 660 430 75 270 160 260 1930 210 1430 470 2019
176 2019Q3 Job Destruction 4680 680 680 50 240 160 160 1170 210 940 340 2019
177 2019Q4 Job Destruction 5480 640 450 45 190 240 320 1820 230 1200 430 2019
178 2020Q1 Job Destruction 5010 630 470 40 210 120 310 1320 250 1250 410 2020
179 2020Q2 Job Destruction 4520 760 520 70 180 150 280 1600 310 1440 440 2020
180 2020Q3 Job Destruction 4190 580 400 25 210 160 200 1120 210 1200 280 2020
181 2020Q4 Job Destruction 3470 430 350 40 150 140 220 1130 230 850 280 2020
182 2021Q1 Job Destruction 5100 660 330 55 200 210 200 1450 230 1080 350 2021
183 2021Q2 Job Destruction 4950 920 690 45 250 180 260 2130 230 1870 450 2021
184 2021Q3 Job Destruction 3060 660 630 50 170 170 260 920 210 970 310 2021
185 2021Q4 Job Destruction 3340 560 690 35 150 100 210 970 190 1030 280 2021
186 2022Q1 Job Destruction 3950 700 450 55 190 170 170 1500 380 1110 380 2022
187 2022Q2 Job Destruction 4450 810 490 75 230 140 280 1970 310 1090 390 2022
188 2022Q3 Job Destruction 4940 690 670 85 210 120 210 1180 230 1020 380 2022
189 2022Q4 Job Destruction 4100 520 460 130 140 140 230 1020 190 880 360 2022
InΒ [90]:
grouped_auckland = regionjob.groupby(['Auckland','Type'])
grouped_auckland.groups
Out[90]:
{(2000, 'Job Destruction'): [97], (2220, 'Job Destruction'): [109], (2250, 'Job Destruction'): [105], (2390, 'Job Destruction'): [117], (2440, 'Job Destruction'): [121], (2510, 'Job Destruction'): [96], (2580, 'Job Destruction'): [120], (2600, 'Job Destruction'): [100], (2610, 'Job Creation'): [15], (2620, 'Job Destruction'): [124], (2630, 'Job Destruction'): [153], (2660, 'Job Destruction'): [104], (2680, 'Job Destruction'): [99], (2700, 'Job Destruction'): [116], (2710, 'Job Creation'): [1], (2720, 'Job Destruction'): [112, 145], (2770, 'Job Destruction'): [107, 141], (2820, 'Job Destruction'): [161], (2860, 'Job Destruction'): [101], (2870, 'Job Creation'): [18], (2880, 'Job Destruction'): [148], (2950, 'Job Destruction'): [129], (2960, 'Job Destruction'): [113], (3010, 'Job Creation'): [19], (3040, 'Job Creation'): [2], (3060, 'Job Creation'): [6, 10, 42], (3060, 'Job Destruction'): [184], (3080, 'Job Destruction'): [108], (3090, 'Job Creation'): [53], (3130, 'Job Creation'): [12], (3130, 'Job Destruction'): [114], (3140, 'Job Destruction'): [152], (3150, 'Job Creation'): [41], (3180, 'Job Destruction'): [149, 165], (3250, 'Job Destruction'): [143], (3260, 'Job Creation'): [37], (3260, 'Job Destruction'): [156], (3280, 'Job Creation'): [9], (3290, 'Job Creation'): [21], (3290, 'Job Destruction'): [111, 169], (3300, 'Job Destruction'): [125], (3330, 'Job Creation'): [50], (3330, 'Job Destruction'): [118, 157], (3340, 'Job Creation'): [3, 5], (3340, 'Job Destruction'): [110, 185], (3360, 'Job Creation'): [38, 39], (3360, 'Job Destruction'): [140], (3370, 'Job Creation'): [22], (3420, 'Job Creation'): [30], (3440, 'Job Destruction'): [128], (3450, 'Job Creation'): [46], (3470, 'Job Destruction'): [115, 181], (3490, 'Job Creation'): [14], (3490, 'Job Destruction'): [127], (3510, 'Job Destruction'): [132, 155], (3520, 'Job Creation'): [85], (3520, 'Job Destruction'): [164], (3570, 'Job Destruction'): [131], (3580, 'Job Destruction'): [173], (3590, 'Job Creation'): [49], (3600, 'Job Destruction'): [133, 137], (3680, 'Job Creation'): [13], (3680, 'Job Destruction'): [103], (3700, 'Job Creation'): [4, 23, 31], (3740, 'Job Destruction'): [119], (3780, 'Job Destruction'): [151], (3790, 'Job Destruction'): [150], (3810, 'Job Creation'): [26], (3820, 'Job Creation'): [40], (3820, 'Job Destruction'): [102], (3840, 'Job Destruction'): [136], (3850, 'Job Destruction'): [160], (3860, 'Job Creation'): [58], (3880, 'Job Creation'): [25], (3890, 'Job Creation'): [35], (3920, 'Job Creation'): [17, 27], (3920, 'Job Destruction'): [147], (3930, 'Job Destruction'): [166, 168], (3950, 'Job Destruction'): [186], (3970, 'Job Creation'): [57], (3980, 'Job Creation'): [33], (4000, 'Job Creation'): [8], (4020, 'Job Creation'): [59], (4030, 'Job Creation'): [29], (4040, 'Job Creation'): [61], (4060, 'Job Destruction'): [122], (4080, 'Job Creation'): [86], (4090, 'Job Destruction'): [163], (4100, 'Job Destruction'): [167, 189], (4130, 'Job Destruction'): [144, 162], (4150, 'Job Destruction'): [130], (4160, 'Job Creation'): [87], (4190, 'Job Creation'): [69], (4190, 'Job Destruction'): [180], (4200, 'Job Creation'): [24, 77], (4220, 'Job Creation'): [81], (4240, 'Job Destruction'): [126], (4250, 'Job Creation'): [55, 66], (4290, 'Job Destruction'): [138], (4350, 'Job Creation'): [82], ...}
InΒ [91]:
grouped_auckland.groups[3290, 'Job Destruction']
Out[91]:
Index([111, 169], dtype='int64')
InΒ [92]:
regionjob1=regionjob.pivot_table(values=['Auckland'], index=['Type'], columns='Year')
regionjob1
Out[92]:
Auckland
Year 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
Type
Job Creation 2875.0 3360.0 3775.0 3680.0 3647.5 3632.5 3897.5 4107.5 4345.0 3985.0 3347.5 4532.5 4327.5 4315.0 4580.0 4717.5 4637.5 4610.0 5512.5 5665.0 4792.5 5017.5 5745.0 5935.0
Job Destruction 2255.0 3300.0 3102.5 3190.0 3077.5 2922.5 3022.5 3687.5 3530.0 3707.5 4377.5 3845.0 3635.0 3602.5 3335.0 3985.0 4050.0 3730.0 3812.5 4345.0 5050.0 4297.5 4112.5 4360.0

3.6.2 Visualize the pivot table.ΒΆ

InΒ [93]:
years = regionjob1.columns.levels[1].tolist()  
job_creation = regionjob1.loc['Job Creation'].values.flatten().tolist() 
job_destruction = regionjob1.loc['Job Destruction'].values.flatten().tolist()  

plt.figure(figsize=(12, 6))
plt.plot(years, job_creation, label='Job Creation', marker='o')
plt.plot(years, job_destruction, label='Job Destruction', marker='x')

plt.title('Job Creation and Destruction in Auckland from 1999 to 2022')
plt.xlabel('Year')
plt.ylabel('Number of Jobs')
plt.xticks(years, rotation=45)  
plt.legend()
Out[93]:
<matplotlib.legend.Legend at 0x2ca66e42050>
No description has been provided for this image

Based on the table, we can see that from 1999 to 2022, the number of jobs created in Auckland is mostly greater than the number of jobs destroyed, indicating a generally positive trend in the job market. However, there are two points in time, 1999 and 2019, where job destruction exceeded job creation. This could be due to specific policies implemented in Auckland or the impact of the COVID-19 pandemic causing these changes.

3.7 What are the Employment Prospects and Highest Salary Ranges for the Manufacturing and Technology Industries, as well as for Data Analyst Positions?ΒΆ

3.7.1 Group by the Manufacturing and technology industry with Good Job Opportunities.ΒΆ

InΒ [94]:
df_jobprofiles
Out[94]:
Occupation Sub Title Description Job Opportunities Training Required Industry Earnings
0 Energy/​Carbon Auditor Kaitātari PΕ«ngao/​Waro Energy/carbon auditors assess the amount of energy used and carbon produced by organisations. They also recommend ways to increase energy efficiency. Average 2-3 years Construction and infrastructure, Services industries $60K-$80K per year, $80K-$200K per year
1 Television Presenter Kaipānui Pouaka Whakaata Television presenters introduce, present or host programmes on television. Poor N/A Creative industries
2 Facilities Manager Kaiwhakahaere Whakaurunga Facilities managers co-ordinate the strategic and operational management of buildings and facilitiesΒ to ensure they are safe, healthy, sustainable, productive and fit-for-purpose. Good N/A Construction and infrastructure $55K-$100K per year, $100K-$150K per year
3 Aircraft Refueller KaiwhakakΔ« Waka Rererangi Aircraft refuellers fill aircraft with fuel at airports. Poor <1 year Services industries $55K-$65K per year, $65K-$75K per year
4 Electronics Engineer Mataaro Tāhiko Electronics engineers design and oversee production of electronic equipment such as radios, televisions, computers, washing machines and telecommunication systems. They may also work in sales and technical support. Good 4 years Manufacturing and technology $100K per year
5 Dairy Processing Operator Kaiwhakamahi PΕ«rere Hua Miraka Dairy processing operators oversee the operation of equipment used to produce a wide range of dairy products such as cheese, butter, yoghurt and milk powder. Good N/A Manufacturing and technology, Primary industries $50K-$60K per year, $70K-$80K per year
6 Trainer Kaiwhakangungu/​Kaiwhakaako Trainers plan and provide training courses for employees of businesses, government and other organisations. Good N/A Services industries $56K-$82K per year, $82K-$128K per year
7 Mortgage Broker Kaitakawaenga PΕ«tea Taurewa Mortgage brokers offer financial advice to people wanting to buy a house or property and help them apply for a home loan. Good <1 year Services industries $70K-$100K per year
8 Insurance Loss Adjuster Kaiwhakatika Makeretanga RΔ«anga Insurance loss adjusters investigate and calculate insurance claims. Average 0-3 years Services industries $56K-$88K per year, $80K-$143K per year
9 Agricultural/​Horticultural Field Representative Māngai Taiao Ahuwhenua Agricultural/horticultural field representatives sell products such as farm equipment, and advise clients on crop and livestock management. Good N/A Primary industries $55K-$90K per year, $90K-$120K per year
10 Statistician Kaitatau Statisticians design studies and surveys, and collect, analyse, interpret and present numerical information to assist in decision-making. Good 4 years Services industries, Social and community services $55K-$65K per year, $75K-$140K per year
11 Artistic Director KaihautΕ« Toi Artistic directors plan and directΒ the activities of performing arts organisations such as theatre and dance companies, and arts activities at festivals and venues. Poor N/A Creative industries
12 Director (Film, Television, Radio or Stage) Kaitohu (Kiriata, Pouaka Whakaata, Irirangi, Whakaari rānei) Directors instruct cast and crew and oversee the artistic and production aspects of film, television, radio or stage creations. Poor N/A Creative industries
13 Forestry Scientist KaipΕ«taiao Ngahere Forestry scientists research forest growth, wood processing, conservation and different types of trees, and how these can be used. Good 3-4 years Primary industries $55K-$98K per year, $110K-$150K per year
14 Tattoo Artist Ringa Kirituhi Tattoo artists use sterilised skin-piercing equipment and ink or jewellery to decorate people's skin. Poor <1 year Creative industries
15 Minister of Religion Amorangi Ministers of religion provide leadership, guidance and training for members of a religious group. Poor N/A Social and community services
16 Energy and Chemical Plant Operator Kaiwhakahaere Rawa PΕ«ngao, Rawa MatΕ« Energy and chemical plant operators monitor, control and maintain machinery and equipment at industrial sites such as power stations. Average N/A Manufacturing and technology $70K-$80K per year, $80K-$180K per year
17 Forest Manager Kaimahi Ngahere Forest managers plan and direct the planting, growth, harvesting and protection of forests for wood production. Good 3 years Primary industries $55K-$95K per year, $95K-$150K per year
18 Epidemiologist Kaimātai Tahumaero Epidemiologists study the causes, transmission and distribution of diseases in population groups to inform public health programmes and prevent the spread of disease. Good 5 years Social and community services $50K-$86K per year, $75K–$175K per year
19 Wool Classer Kaimāhiti Wūru Wool classers sort wool into categories. They ensure wool is clean, identified and documented for sale. Average N/A Primary industries $50-$60 per hour
20 Agricultural/​Horticultural Scientist KaipΕ«taiao Ahuwhenua Agricultural/horticultural scientists study farm animals, soils, pastures and crops to improve growth, health and quality, and to prevent pests and disease. Good 5-9 years Primary industries $65K-$75K per year, $80K-$150K per year
21 Geophysicist Kaimātai Pūtaiao Whenua Geophysicists use data-collecting technology to study natural processes of the Earth, such as earthquake and volcanic activity, and to locate minerals, oil and gas, or groundwater. Average 3-7 years Primary industries, Services industries $65K-$75K per year, $120K-$180K per year
22 Workplace Relations Adviser Kaitohutohu Takawaenga Mahi Workplace relations advisers provide advice and mediation to different groups in the workplace to prevent and resolve workplace disputes. Average 2-3 years Services industries $92K-$122K per year, $102K-$163K per year
23 Telecommunications Engineer Mataaro Whitiwhiti Kōrero Telecommunications engineers design, test and build telecommunications networks and systems. Good 2-4 years Manufacturing and technology $60K-$65K per year, $65K-$140K per year
24 Valuer Kaiwhakatau Wāriu Valuers assess the value of real estate or personal property such as art and jewellery, for sales, rentals, mortgages, insurance or rates. Good 1-4 years Services industries $56K-$87K per year, $87K-$138K per year
25 Auctioneer Māngai Hokohoko Auctioneers take charge of public or private auctions. They sell goods, property or livestock on behalf of the owner to people offering the highest price. Poor N/A Services industries
26 Information Technology Manager Kaiwhakahaere Hangarau Pārongo Information technology (IT) managers plan and supervise computer and information technology services for organisations or technical teams. Good 2-3 years Manufacturing and technology, Services industries $100K-$190K per year, $150K-$350K per year
27 Judge Kaiwhakawā Judges listen to court cases and make decisions on matters of law. Poor 11 years Social and community services $370K-$578K per year
28 Chemical Engineer Mataaro MatΕ« Chemical engineers design, develop and operate equipment and processes used to manufacture chemicals and products. Good 3-4 years Manufacturing and technology $100K per year
29 Curator Kaitiaki Taonga Curators research, develop, exhibit and maintain collections for museums, art galleries and artists. Poor 5 years Social and community services, Services industries, Creative industries $55K-$65K per year, $65K-$95K per year
30 Tertiary Lecturer Pūkenga Whare Wānanga Tertiary lecturers teach at universities, Te Pūkenga, wānanga and other post-secondary education providers. They also carry out research and do administrative tasks. Good 1-8 years Social and community services $50K-$108K per year, $77K-$213K per year
31 Environmental Scientist KaipΕ«taiao Ao TΕ«roa Environmental scientists study human effects on the environment such as climate change, pollution and loss of biodiversity. They also advise on how to avoid or reduceΒ these harmful effects. Good 3-9 years Primary industries, Social and community services $58K-$165K per year
32 Wall and Floor Tiler Kaiwhakatakoto Taera Pakitara, Taera Papa Wall and floor tilers lay ceramic, clay, slate, marble and glass tiles. Good N/A Construction and infrastructure $23-$25 per hour, $25-$35 per hour
33 Watchmaker and Repairer Kaihanga/​Kaiwhakatika Karaka/​Matawā Watchmakers and repairers clean, repair and assemble mechanical or electronic timepieces such as watches and clocks. Poor 4 years Manufacturing and technology, Services industries $47K-$55K per year, $80K per year
34 Acupuncturist Kaiwero Ngira Hauora Acupuncturists give general health advice and treat patients using therapies such as electronic and needle acupuncture, cupping, skin scraping (gua sha), the heating of acupuncture points (moxibustion) and tuina (massage). Average 4 years Social and community services $47K-$100K per year
35 Vehicle Groomer/​Cleaner Kaiwhakapaipai Waka Vehicle groomers/cleaners clean and polish vehicles, and mayΒ also drive, park and maintain vehicles. Good N/A Services industries $23 per hour
36 Brick and Blocklayer Ringa Tiri Pereki/​Ringa Tiri Poraka Brick and blocklayers lay bricks, concrete blocks and tiles to construct or repair buildings, walls, arches, chimneys or paved areas. Good 2-3 years Construction and infrastructure $23-$25 per hour, $25-$60 per hour
37 Building Insulator Kaitauārai Whare Building insulators install or apply special material to buildings or equipment to prevent or reduce heat, cold, air, sound or moisture loss. Good <1 year Construction and infrastructure $23-$24 per hour, $23-$30 per hour
38 Carpet Cleaner Kaiwhakapai Whāriki Carpet cleaners clean carpets, floors and upholstery, and may dry and clean carpets after floods. Good <1 year Services industries $23-$30 per hour
39 Textile Process Operator Kaimahi Kaka-aku Textile process operators carry out a variety of tasks in the production of materials such as fabric, canvas, yarn and carpet. Poor <1 year Manufacturing and technology $23-$24 per hour
40 Cleaner Kaihoroi Whare Cleaners clean offices, factories, shops, public buildings, schools, private homes and aircraft. Good N/A Services industries $23-$24 per hour
41 Tailor/​Dressmaker Kaihanga Kākahu Tailors/dressmakers design, make, alter and repair clothing. Poor N/A Services industries, Creative industries $23 per hour
42 Taxi Driver/​Chauffeur Kaitaraiwa Waka Pāhihi Taxi drivers/chauffeurs drive vehicles to transport passengers from one place to another. Good N/A Services industries $23-$25 per hour
43 Street/​Park Cleaner Kaitahitahi Papa RΔ“hia/​Huarahi Street/park cleaners clean and maintain public areas such as streets, parks and buildings. Poor N/A Services industries $23-$26 per hour
44 Service Station Attendant Kaihoko Penehini/​Hinu Service station attendants help customers get petrol, gasΒ or oil for their vehicle, and sell motoring accessories and food items. Average N/A Services industries $23 per hour
45 Roofer Kaihanga Tuanui Roofers repair or install roofs using materials such as roofing iron, tiles and shingles. Good 3 years Construction and infrastructure $23-$30 per hour, $30-$40 per hour
46 Concrete Worker Kaimahi Raima Concrete workers make, pour, spread, finish, reinforce and cut concrete for construction projects such as buildings and footpaths. Good N/A Construction and infrastructure $23-$30 per hour
47 Plastics Technician Kaihangarau Kirihou Plastics technicians set up, adjust, maintain and repair machines that manufacture plastic products. Good 3 years Manufacturing and technology $23-$25 per hour, $25-$34 per hour
48 Plastics Worker Kaimahi Kirihou Plastics workers operate the machinery that makes, assembles and repairs plastic, composite and rubber products. Average <1 year Manufacturing and technology $23-$25 per hour
49 Patternmaker Ringa Tauira Kākahu Patternmakers turn clothing designs into patterns. Average 1-2 years Manufacturing and technology, Creative industries $23-$36 per hour
50 Plasterer Kaiwhakapiri Uhi Plasterers apply plaster or other materials to buildings. They usually specialise in either interior or exterior plastering. Good 1-3 years Construction and infrastructure $23-$30 per hour
51 Photographer Kaitango Whakaahua Photographers take photographs of people, places, products or events. Average N/A Creative industries $23-$35 per hour, $50-$250 per hour
52 Fencer Kaihanga Taiapa Fencers construct and repairΒ fences, barriers or walls made of timber, metal, wire, chain-link or other materials. Good N/A Primary industries, Construction and infrastructure $23-$30 per hour, $45-$62 per hour
53 Mail and Parcel Sorter Kaimāhiti Reta/​PΕ«hera Mail and parcel sorters work in processing centres. They sort mail and parcels by address, either manually or by machine. Average <1 year Services industries $23 per hour
54 Locksmith Kaimahi Raka Locksmiths install, maintain and replace locks, keys, safes, electronic locking devices and access control systems for buildings and vehicles. They may also install and repair security systems. Average 3 years Construction and infrastructure, Services industries, Manufacturing and technology $23-$25 per hour, $25-$29 per hour
55 Flooring Installer Kaiwhakauru Uhi Flooring installers lay, replace and repair floor coverings such as carpet, linoleum, vinyl and timber. Good 2-3 years Construction and infrastructure $23-$24 per hour, $25-$35 per hour
56 Metal Worker Kaimahi Maitai Metal workers make patterns and moulds for metal castings, heat and hammer metal into shape, and repair metal parts and equipment. Poor 4 years Construction and infrastructure, Manufacturing and technology $23-$29 per hour
57 Lighting Technician Kaihangarau Rama Lighting technicians set up and operate lighting equipment to provide light and special lighting effects in theatres, at events, and for film and television productions. Poor N/A Services industries, Creative industries $23-$27 per hour, $36-$70 per hour
58 Laundry Worker/​Dry-cleaner Kaimahi Horoi Kākahu/​Kaiwhakamohani Kākahu Laundry workers/dry-cleaners clean, wash and care for clothing, curtains and bedding. Good N/A Services industries $23-$30 per hour
59 Forklift Operator Kaitaraiwa Waka Uta Forklift operators operate vehicles that have a lifting platform for shifting and stacking heavy articles such as pallets, bales, crates, containers or cartons. Good <1 year Construction and infrastructure, Manufacturing and technology $23-$24 per hour
60 Glass Processor Kaiwhakarite Karaehe Glass processors prepare and process sheets of flat glass into products such as windows and mirrors for installation in buildings and related structures. Good 3 years Manufacturing and technology $23-$25 per hour, $23-$36 per hour
61 Groundsperson Kaimanaaki Papa Groundspeople are in charge of the turf (grass), tracks and pitches at sports fields, golf courses, public areas, schools and racecourses. Good N/A Primary industries $47K-$60K per year, $60K-$100K per year
62 Glazier Kaimahi Karaehe Glaziers install or replace glass or mirrors in buildings, vehicles or boats and may create decorative glass features. Good 1-3 years Construction and infrastructure $23-$30 per hour
63 Furniture Packer/​Mover Kaiuta/​Kaiwhakaneke Taonga Furniture packers/movers pack furniture and equipment and move it between households, offices and storage places. Good N/A Services industries $23-$26 per hour
64 Importer/​Exporter Kaiwhiwhi Rawa i Tāwāhi/​Kaituku Rawa Ki Tāwāhi Importers/exporters plan, organise, direct and co-ordinate the operations of an importing or exporting business. Average N/A Services industries, Manufacturing and technology $47K-$75K per year
65 Fire Engineer Mataaro Δ€rai Ahi Fire engineers plan and design safety features that detect, control or reduce fire and smoke in buildings and structures. They also analyse how fire behaves and how safety features perform in fire. Good 6 years Construction and infrastructure $70K-$90K per year, $120K-$180K per year
66 Recycler/​Dismantler Kaihangarua/​Kaiwetewete Recyclers/dismantlers take apart, separate, sort and sell materials to be recycled or reused. Good N/A Manufacturing and technology $23-$25 per hour
67 Parking Officer Δ€piha Atoato Waka Parking officers give fines for illegal parking and vehicle offences such as unregistered cars. Poor N/A Social and community services $25 per hour
68 Optometrist Kaimātai Whatu Optometrists examine clients' eyes to diagnose and provide solutions for vision problems. They also diagnose, monitor and manage eye diseases such as cataracts. Good 5 years Social and community services $65K-$114K per year, $114K-$225K per year
69 Heavy Truck Driver Kaitaraiwa Taraka Taumaha Heavy truck drivers drive trucks with or without trailers. They may transport materials, livestock, general freight, and hazardous substances or spread fertiliser. Good 1-2 years Services industries $23-$35 per hour, $35-$45 per hour
70 Tow Truck Operator Kaitaraiwa Taraka Tow truck operators drive and operate trucks to tow vehicles that have broken down, been damaged or illegally parked. Good <1 year Services industries $23-$25 per hour, $25-$35 per hour
71 Bus Driver Kaitaraiwa Pahi Bus drivers operate buses and drive passengers along local, chartered or intercity routes. Good <1 year Services industries $27-$30 per hour
72 Teacher of English to Speakers of Other Languages (ESOL) Kaiako Reo Pākehā (ki te Hunga Kōrero Reo KΔ“) Teachers of English to speakers of other languages (ESOL teachers) teach people from non-English speaking backgrounds how to speak, read and write English. Poor 1-5 years Social and community services $48K-$80K per year
73 Private Teacher/​Tutor Kaiako Whaiaro Private teachers/tutors teach a specific skill or subject to individuals or small groups of children or adults. Average 0-3 years Social and community services, Services industries $28-$39 per hour, $60-$80 per hour
74 Pest Control Technician Kaihangarau Patu Orotā Pest control technicians identify and remove pests, such as insects, rats and mice, from buildings and properties. Good 1-2 years Services industries $23-$34 per hour
75 Automotive Electrician Kaimahi Hiko ā-Waka Automotive electricians install, maintain and repair electrical wiring, parts and electrical and electronic systems in vehicles. Good 3-4 years Manufacturing and technology $32-$45 per hour
76 Coachbuilder/​Trimmer Kaihanga Pahi/​Kaiwhakarākei Waka Coachbuilders manufacture and assemble frames, panels and parts for vehicles such as buses and motor homes. Vehicle trimmers install and repair the upholstery of vehicles. Good 3 years Manufacturing and technology $23 per hour, $23-$26 per hour
77 Tyre Technician Kaiwhakamau Taea Tyre technicians remove, repair and fit tyres for all types of vehicles. They also advise customers on different types of tyres, and check and adjust vehicle wheel alignment. Average N/A Manufacturing and technology, Services industries $23-$26 per hour
78 Environmental Engineer Mataaro Taiao Environmental engineers assess and reduce the impact of engineering projects on water, soil and air. They also plan and design systems to treat and remove waste. Good 3-4 years Construction and infrastructure, Primary industries, Manufacturing and technology $65K-$140K per year
79 Make-up Artist Kaitoi Whakapaipai Kanohi Make-up artists apply make-up to enhance or alter people's appearances. Good <1 year Services industries, Creative industries
80 Florist Kaihoko Putiputi Florists sell plants and fresh flowers and use them to design and create floral arrangements. Average N/A Services industries, Creative industries $23-$24 per hour, $25-$28 per hour
81 Aircraft Loader Kaiuta/​Kaihoroi Waka Rererangi Aircraft loaders load and unload aircraft, and transfer freight and baggage between airport buildings and aircraft. Average N/A Services industries $23-$29 per hour
82 Contact Centre Worker Kaimahi Pokapū Whakapā Contact centre workers answer enquiries and provide or organise help for those who contact them. They may also deal with customer complaints, or sell goods or services. Good N/A Services industries $49K-$65K per year, $65K-$200K per year
83 Podiatrist Rata Waewae Podiatrists diagnose, treat and prevent foot and lower limb problems. Good 3 years Social and community services $50K-$70K per year, $70K-$98K per year
84 Medical Laboratory Scientist Kaipūtaiao Taiwhanga Rongoā Medical laboratory scientists carry out laboratory tests on blood, tissues and other samples taken from patients. Good 4 years Social and community services, Manufacturing and technology $52K-$73K per year, $77K-$106K per year
85 Professional Sportsperson Kaiwhakataetae Ngaio Professional sportspeople take part in competitive national and international sports such as rugby, football, cricket, netball, golf, tennis and horse racing. Poor N/A Services industries
86 Primary School Teacher Kaiako Kura Tuatahi Primary school teachers teach children between the ages of five and 13 at primary or intermediate schools. Good 3-4 years Social and community services $55K-$95K per year
87 Air Traffic Controller Kaiwhakahaere Huarahi Rererangi Air traffic controllers direct the safe and orderly movement of aircraft while they are flying, landing, taking off and taxiing. Poor 1-2 years Services industries $110K-$245K per year, $160K-$245K per year
88 Horse Trainer Kaiwhakapakari Hōiho Horse trainers train horses for racing, and are responsible for their care at a stable or race track. Average N/A Primary industries
89 Auditor Kaitātari Kaute Auditors examine and report on the financial records and systems of organisations to ensure they are accurate. Good 3-6 years Services industries $66K-$92K per year, $92K-$184K per year
90 Hotel/​Motel Manager Kaiwhakahaere Hōtera/​Mōtera Hotel/motel managers plan, organise and control the operation of a hotel, motel or hostel, including management of staff. Good N/A Services industries $60K-$80K per year, $80K-$100K per year
91 Electrical Engineer Mataaro PΕ«hiko Electrical engineers design, construct and manufacture electrical systems. They also maintain, operate and manage these systems. Good 4 years Manufacturing and technology $77K-$160K per year, $150K-$210K per year
92 General Practitioner Rata Hauora General practitioners care for, diagnose and treat the health problems of people and families in the community. Good 12 years Social and community services $187K per year
93 Train Driver Kaitaraiwa Rerewhenua Train drivers drive passenger or freight trains. They may shift (shunt) carriages and wagons using trains or remote controls. Average 1 year Services industries $27-$43 per hour, $47-$58 per hour
94 Butcher Ringa Tapahi MΔ«ti Butchers cut, prepare and sell meat. Good 3-4 years Manufacturing and technology, Services industries $26-$44 per hour
95 Network Administrator Kaiwhakahaere Whatunga Network administrators design, install and maintain computer hardware and software networks, from one-building LANs (local area networks) to worldwide WANs (wide area networks). Good 1-3 years Manufacturing and technology $80K-$140K per year
96 Science Technician Kaihangarau PΕ«taiao Science technicians help scientists carry out research, testing and experiments in areas such as chemistry, earth sciences, life sciences and physical sciences. Good 2-5 years Manufacturing and technology, Primary industries $52K per year
97 Cafe/​Restaurant Manager Kaiwhakahaere Toa Kawhe/​Wharekai Cafe/restaurant managers are in charge of running cafes, restaurants and fast food outlets. They may also run catering businesses. Good N/A Services industries $28 per hour, $36 per hour
98 Early Childhood Teacher Kaiako Kōhungahunga Early childhood teachers educate and care for young children in kindergartens, kōhanga reo or childcare centres. Kōhanga reo kaiako also help children learn te reo Māori and tikanga Māori (culture and customs). Good 3-4 years Social and community services $57K-$100K per year
99 Helicopter Pilot Kaiwhakarere Toparere Helicopter pilots fly helicopters to transport goods, people including photographers, spread fertiliser, spray crops, lift items into difficult sites and provide air rescue and ambulance services. Good <2 years Services industries , $55K-$200K per year
100 Storeperson Tangata Whakaputu Storepeople receive, check, store and send out goods from a warehouse, business or organisation. Good N/A Services industries $23-$30 per hour
101 Kaiwhakaako Māori Kaiwhakaako Māori Kaiwhakaako Māori teach in te reo Māori at primary and secondary schools. Good 3-4 years Social and community services $55K-$103K per year
102 Secondary School Teacher Kaiako Kura Tuarua Secondary school teachers plan, prepare and teach one or more subjects to students between the ages of 13 and 18. Good 4 years Social and community services $49K-$89K per year, $57K-$103K per year
103 Medical Laboratory Technician Kaihangarau Taiwhanga Rongoā Medical laboratory technicians take medical samples and run tests under the supervision of scientists and pathologists. Average 2 years Social and community services $48K-$67K per year
104 Phlebotomist Kaitiki Toto Phlebotomists collect blood and samples from patients for laboratory testing or for blood banks. Average 2 years Social and community services $52K-$82K per year
105 Air Force Aviator Paerata Tauārangi Air force aviators defend their country, keep the peace and provide disaster relief. Average <1 year Services industries, Social and community services $51K per year, $101K-$120K per year
106 Air Force Officer Δ€piha Tauārangi Air force officers plan and supervise flying missions, repair and maintenance of planes, helicopters and radio equipment. Average <1 year Services industries, Social and community services $51K per year, $101K-$133K per year
107 Navy Sailor Kaumoana Tauā Moana Navy sailors defend their country, keep the peace, patrol borders and provide disaster relief. Good <1 year Services industries, Social and community services $51K per year, $101K-$120K per year
108 Navy Officer Δ€piha Tauā Moana Navy officers train navy sailors, manage field exercises and lead sailors in combat, peacekeeping missions, border patrols and disaster relief. Average <1 year Services industries, Social and community services $51K per year, $101K-$133K per year
109 Orchard Farmer/​Manager Kaipāmu Uru Hua Rākau/​Kaiwhakahaere Uru Hua Rākau Orchard farmers/managers plan and manage fruit and nut productionΒ in orchards. Average N/A Primary industries $55K-$110K per year, $120K-$180K per year
110 Nursery Grower/​Worker Kaiwhakatipu/​Kaimahi Otaota Nursery growers/workers grow young plants, flowers, trees and shrubs for sale or for use in parks and gardens. Average N/A Primary industries $23-$25 per hour, $25-$31 per hour
111 Crop Farmer/​Manager Kaiahuwhenua Huangakai/​Kaiwhakahaere Huangakai Crop farmers/managers plan and manage plant production on farms and in vineyards and hothouses. Good N/A Primary industries $55K-$110K per year, $120K-$180K per year
112 Crop Worker Kaimahi Huangakai Crop workers assist with the growing and harvesting of fruit, vegetables and other produce on farms, market gardens, orchards and vineyards. Good N/A Primary industries $23-$24 per hour, $23-$25 per hour
113 Interpreter Kaiwhakawhiti Reo ā-Waha Interpreters convert what people say from one language into another. Average 3 years Social and community services $35-$80 per hour, $80-$140 per hour
114 Translator Kaiwhakawhiti Reo ā-Tuhi Translators convert written material from one language into another. Poor 3 years Services industries, Social and community services
115 Conservator Kaiatawhai Whakaora Taonga Conservators help preserve art and other important historical items by preventing deterioration and repairing damage. Poor 5 years Social and community services, Services industries, Creative industries $55K-$70K per year, $70K-$82K per year
116 Midwife Tapuhi ā-Whare Midwives provide care and support to pregnant people, their partners and family/whānau during pregnancy, labour and birth, and for six weeks following the birth. Good 3-4 years Social and community services $77K-$111K per year, $102K-$153K per year
117 Data Analyst Kaitātari Raraunga Data analysts identify and describe data trends using statistics and specialised software to help organisations achieve their business aims. Good 2-3 years Manufacturing and technology $90K-$120K per year, $110K-$170K per year
118 Personal Trainer/​Exercise Professional Kaiwhakangungu Tinana/​Mahi Ngaio Personal trainers/exercise professionals provide expertise, knowledge and structured support to improve and maintain health and wellness through physical activity. Good N/A Services industries $23-$70 per hour, $45-$90 per hour
119 User Experience Designer Kaihoahoa Wheako Whakamahi User experience (UX) designersΒ design how products such as websites and apps look and work, based on users' needs. Good 1-3 years Manufacturing and technology $100K-$175K per year
120 Veterinarian PΕ«kenga Hauora Kararehe Veterinarians treat sick and injured animals, provide general animal care, and advise about health care and disease prevention for pets and farm (production) animals. Good 5 years Services industries, Social and community services, Primary industries $75K-$105K per year, $130K-$220K per year
121 Paramedic Δ€piha Whakaora Paramedics assess and treat people who are seriously ill or injured, and transport them to hospital if necessary. Good 3 years Social and community services $84K-$92K per year, $112K-$123K per year
122 Community Development Worker Kaiāwhina Whakawhanake Hapori Community development workers support people to develop and implement plans to make improvements in their community. Good N/A Social and community services $60K-$80K per year
123 Information Technology Architect Kaihoahoa Hangarau Pārongo Information technology (IT) architects analyse an organisation's IT needs, recommend solutions and oversee their delivery and implementation. Good 3-5 years Manufacturing and technology $140K-$200K per year
124 Project Manager Kaiwhakahaere Kaupapa Project managers manage the planning, resourcing, scheduling and administration of projects to deliver them on time and within budget. Good 2-3 years Construction and infrastructure, Services industries, Manufacturing and technology $90K-$140K per year, $100K-$170K per year
125 Medical Imaging Technologist Kairahurahu Whakaahua Whakaora Medical imaging technologists use x-ray and other imaging equipment to take images of injuries and diseases. Good 3-5 years Social and community services $63K-$84K per year, $87K-$118K per year
126 Aeronautical Engineer Mataaro Whakahaere PΕ«kaha Rererangi Aeronautical engineers plan and supervise the design, development and modification of all types of flight vehicles. They also monitor and analyse in-service failures and faults. Good 3-4 years Manufacturing and technology, Services industries $100K per year
127 Systems Administrator Kaiwhakahaere PΕ«naha Systems administrators develop, maintain and administer computer operating systems, database management systems, and security policies and procedures. Good 2-4 years Manufacturing and technology $85K-$120K per year, $85K-$145K per year
128 Boat Builder Kaihanga Waka Boat builders build, repair, and sometimes design boats and their interiors. This can include furnishings, engines, electrics and plumbing. Good 3-4 years Construction and infrastructure, Manufacturing and technology $23-$30 per hour, $30-$50 per hour
129 Naval Architect Kaihoahoa Tāruru Naval architects plan, design and supervise the construction and repair of ships, yachts and boats. Good 4 years Construction and infrastructure, Creative industries $62K-$78K per year, $85K-$150K per year
130 Roadmarker Ringa Tohu Papa Roadmarkers use machines to apply markings to roads and surfaces such as car parks and sports courts. Average N/A Construction and infrastructure $23-$40 per hour
131 Corrections Officer Δ€piha Whare Herehere Corrections officers are responsible for keeping prisoners safe and secure and motivating them to make changes in their lives. Good 1 year Social and community services $60K-$69K per year, $75K-$88K per year
132 Automotive Technician Kaihangarau PΕ«kaha Waka Automotive technicians service and repair vehicles and their parts and systems. Good 3-4 years Manufacturing and technology $25-$40 per hour, $26-$45 per hour
133 Automotive Refinisher Kaipeita Waka Automotive refinishers prepare vehicle surfaces, match and mix colours, and apply paint to vehicles. Good 3-4 years Manufacturing and technology $23-$28 per hour, $28-$38 per hour
134 Carpenter Kaihanga Whare Carpenters work mainly with wood to repair or install foundations, walls, roofs, windows and doors in buildings. Good 3-4 years Construction and infrastructure $23-$25 per hour, $29-$41 per hour
135 Foreign Policy Officer Δ€piha Take Tāwāhi Foreign policy officers represent New Zealand's interests overseas and provide policy advice to the Government on foreign affairs and trade. Average 5 years Social and community services $68K-$75K per year, $120K-$140K per year
136 Crane Operator Kaiwhakamahi Wakaranga Crane operators use cranes to move objects such as materials on construction sites, containers on wharves, and heavy parts in factories. Average 1-2 years Construction and infrastructure, Manufacturing and technology $23-$35 per hour, $60-$70 per hour
137 Counsellor Kaitohutohu Counsellors help people to deal with challenges and manage their emotions, thoughts and behaviour. Good 3-5 years Social and community services $57K-$86K per year, $87K-$119K per year
138 Sales and Marketing Manager Kaiwhakahaere Hokohoko Sales and marketing managers plan and direct the development, promotion and sale of an organisation's goods and services. Good 3 years Services industries, Creative industries $95K-$350K per year, $100K-$258K per year
139 Marine Biologist Kaimātai Koiora Moana Marine biologists study animals and plants that live in the sea and freshwater, and how they interact with their surroundings. Average 5-9 years Primary industries $71K- $177K per year, $58K- $198K per year
140 Dentist Ngaio Niho Dentists study and treat diseases, injuries and problems of the mouth, teeth, gums and jaw. They also educate patients on how to avoid oral health problems. Average 5 years Social and community services $129K-$254K per year
141 Psychotherapist Kaihaumanu Hinengaro Psychotherapists provide talk therapy to help people manage and improve their mental health and emotional wellbeing. Average 3-5 years Social and community services $68K-$85K per year, $87K-$118K per year
142 Policy Analyst Kaitātari Kaupapa Policy analysts analyse information to assist in the development, interpretation and review of government or industrial policies. Average 3 years Social and community services $71K-$92K per year, $112K-$170K per year
143 Probation Officer Δ€piha Matakana Probation officers supervise offenders serving a sentence in the community. They also help ex-prisoners return to society. Good <1 year Social and community services $54K-$60K per year, $60K-$69K per year
144 Environmental/​Public Health Officer Δ€piha Hauora Taiao/​Pāpori Environmental/public health officersΒ investigate, monitor, assess and advise on food and alcohol safety, disease prevention, disease outbreaks, and environmental hazards such as pollution. Average 3 years Social and community services $48K-$70K per year, $55K-$85K per year
145 Psychologist Kaimātai Hinengaro Psychologists diagnose, treat, and work to prevent a range of psychological problems that affect people's behaviour, thoughts and emotions. Good 6-7 years Social and community services $55K-$70K per year, $68K-$150K per year
146 Clinical Physiologist Kaimātai Hinengaro Tiaki Tūroro Clinical physiologists use technical equipment to measure and analyse patients' organs or internal systems, to help doctors diagnose and treat patients. Good 3-5 years Social and community services $59K-$86K per year, $88K-$119K per year
147 Pharmacist Taka Rongoā Pharmacists prepare and dispense prescribed medicine, and discuss conditions and treatments with patients. They may carry out tests and vaccinations. Good 5 years Social and community services, Services industries $58K-$86K per year, $87K-$119K per year
148 Anaesthetic Technician Kaihangarau Haurehu Anaesthetic technicians assist anaesthetists during operations, and prepare operating theatres and clinics for anaesthetic procedures. Good 3 years Social and community services $52K-$60K per year, $88K-$119K per year
149 Police Officer Pirihimana Police officers work to prevent and solve crime, keep the peace, and respond to criminal activities and emergencies. Average <1 year Social and community services $75K-$83K per year
150 Security Officer/​Guard Δ€piha Whakamarumaru/​TΕ«tei Whakamarumaru Security officers/guards protect people, property and assets by investigating, monitoring, controlling and reporting threats. Good <1 year Social and community services, Services industries $23-$30 per hour, $30-$70 per hour
151 Fishery Officer Δ€piha Hao Ika Fishery officers gather information on all aspects of the fishing industry and enforce fisheries laws. Poor <1 year Primary industries, Social and community services $61K-$100K per year
152 Arborist Kaitiaki Rākau Arborists plant and remove trees, prune branches and treat disease. Good 1-3 years Primary industries $23-$25 per hour, $25-$35 per hour
153 Real Estate Agent Māngai Hoko Whare/​Whenua Real estate agents arrange property and house sales for clients. Average <1 year Services industries
154 Diagnostic Radiologist Kaimātai Tātari Hihi Irirangi Diagnostic radiologists diagnose disease and injury using x-rays, ultrasound, MRI, CT, nuclear medicine and radioactive solutions. Good 13 years Social and community services $64K-$205K per year, $170K-$251K per year
155 Gynaecologist/​Obstetrician Kaimātai Take Wahine/​Whakawhānau Tamaiti Gynaecologists/obstetricians advise, diagnose and treat issues with the female reproductive system, and provide medical care for women before, during and after pregnancy. Good 14 years Social and community services $64K-$205K per year, $170K-$251K per year
156 Anaesthetist Kairehu Anaesthetists give anaesthesia (gas or injections to prevent pain) during surgery and other procedures. They assess patients andΒ resuscitateΒ them if necessary. Good 13 years Social and community services $64K-$205K per year, $170K-$251K per year
157 Ranger Δ€piha Papa Atawhai Rangers protect, enhance and maintain conservation and recreation areas such as regional and national parks, forests, wetlands, reserves, and sites of cultural importance. Average N/A Primary industries, Social and community services $47K-$60K per year, $53K-$93K per year
158 Ophthalmologist Tākuta Whakaora Whatu Ophthalmologists diagnose and treat eye conditions and injuries, and perform eye surgery. Good 13 years Social and community services $64K-$205K per year, $170K-$251K per year
159 Physician Rata Physicians are medical specialists who provide non-surgical advice and treatment to patients referred to them by other doctors. Good 14 years Social and community services $64K-$205K per year, $170K-$251K per year
160 Mechanical Engineer Mataaro PΕ«kaha Mechanical engineers design and give advice on the building and repair of machines and tools. They also investigate problems and faults with machinery, and study ways to improve manufacturing and energy production. Good 3-4 years Manufacturing and technology $85K-$105K per year, $75K-$180K per year
161 Teacher Aide Kaiāwhina Kaiako Teacher aides assist teachers in a classroom by working with students on a one-to-one basis, or in groups. Average N/A Social and community services $23-$37 per hour
162 Building Contractor Kaihanga Whare Building contractors run their own businesses and plan, supervise and work on the construction and alteration of buildings. Good 3-4 years Construction and infrastructure
163 Building and Construction Manager Kaiwhakahaere Hanga Whare Building and construction managers plan, control and co-ordinate civil engineering or building projects, and the resources and people involved. Good 2-4 years Construction and infrastructure $100K-$224K per year
164 Civil Engineering Technician/​Draughtsperson Kaihangarau/​Kaihoahoa Mataaro Metarahi Civil engineering technicians/draughtspeople plan and draw the technical details for building and repairing roads, bridges, buildings and other structures. Good 1-2 years Manufacturing and technology, Construction and infrastructure $50K-$70K per year, $70K-$110K per year
165 Scaffolder Kaihanga Rangitupu Scaffolders design, construct and remove scaffolding around buildings and other structures such as bridges. Good 3-5 years Construction and infrastructure $24-$37 per hour
166 Pet Groomer Kaiwhakapaipai Mōkai Pet groomers clean, trim and shape the hair and nails of animals in salons, mobile grooming vans and pet shops. Good N/A Services industries $23-$30 per hour
167 Veterinary Nurse Tapuhi Kararehe Veterinary nurses help assess, treat and care for sick and injured animals. They also interact with clients and perform receptionist duties. Good 2-3 years Services industries, Social and community services, Primary industries $50K-$60K per year
168 Bartender Kaitiaki Pae Inu Bartenders prepare and serve drinks in bars, restaurants and clubs. Good N/A Services industries $23-$25 per hour, $29 per hour
169 Cafe Worker Kaimahi Toa Kawhe Cafe workers prepare, serve and sell food and drinks to customers at delicatessens, cafes, canteens and takeaway bars. Good N/A Services industries $23-$30 per hour
170 Waiter/​Waitress Kaitiaki TΔ“pu Kai Waiters/waitresses serve food and drinks in restaurants, hotels, clubs and other eating places. Good N/A Services industries $24 per hour
171 Dairy Herd Manager Kaiwhakahaere Māpu Kau Dairy herd managers run daily dairy farming operations such as feeding and milking cows, monitoring animal health and environmental management. Good N/A Primary industries $48K-$88K per year, $51K-$90K per year
172 Dairy Farm Manager Kaiwhakahaere Pāmu Kau Dairy farm managers manage farming operations and staff for dairy farm owners. Good N/A Primary industries $63K-$120K per year, $65K-$160K per year
173 Driller Kaipoka Drillers assemble, position, and operate drilling rigs and related equipment to extract ores, liquids, and gases from the earth. Poor N/A Construction and infrastructure $60K-$70K per year, $70K-$150K per year
174 Mining Engineer Mataaro Waro Mining engineers plan, prepare, design and manage the development of opencast (above ground) or underground mines. Average 4 years Construction and infrastructure, Manufacturing and technology $70-$75K per year, $75K-$210K per year
175 Miner/​Quarry Worker Kaimahi Huke Kōwaro Miners and quarry workers operate machinery, vehicles and equipment to extract and process minerals and rocks. Average N/A Construction and infrastructure $60K-$80K per year, $80K-$150K per year
176 Mine/​Quarry Manager Kaiwhakahaere Huke Kōwaro Mine and quarry managers supervise mine and quarry workers, do safety checks and plan activities in mines and quarries. Average N/A Construction and infrastructure $100K-$155K per year, $130K-$210K per year
177 Farmer/​Farm Manager Kaiahuwhenua/​Kaiwhakahaere Pāmu Farmers/farm managers manage and work on farms. Farmers own or lease the land, while farm managers operate farms for farm owners. Good N/A Primary industries $78K per year
178 Farm Assistant Kaimahi Pāmu Farm assistants help farmers with a variety of tasks, including raising and caring for animals, repairs and maintenance, tractor work and other farming activities. Good N/A Primary industries $55K-$60K per year
179 Financial Adviser Kaiwhakatakoto Kaupapa PΕ«tea Financial advisers give advice about financial planning, investing, insurance and other financial services. Good 1-2 years Services industries $80K-$130K per year, $100K-$150K per year
180 Records Adviser Kaiwhakahaere Kōnae Records advisers create and monitor electronic and paper filing systems so that records can be filed, found, tracked and disposed of. Average N/A Services industries $48K-$58K per year, $100K or more
181 Geologist Kaimātai Aro Whenua Geologists study Earth processes, such as earthquakes, floods and volcanic eruptions, to predict future events. They also advise on natural hazards and how to develop or use the Earth's land and resources. Average 3-7 years Services industries, Primary industries $90K-$180K per year
182 Landscape Architect Kaihoahoa Whenua Landscape architects plan, design and advise on the construction of urban, rural, residential and public landscapes. They also manage and conserve natural or heritage landscapes and public open spaces. Average 4 years Construction and infrastructure, Primary industries, Creative industries $80K-$120K per year
183 Hotel Porter Kaikawe Tueke Hotel porters meet and greet guests, answer enquiries, assist with luggage and park guests' vehicles. Good N/A Services industries $23-$29 per hour
184 Forensic Scientist KaipΕ«taiao Taihara Forensic scientists apply scientific knowledge and skills to investigate crimes and help the police find or eliminate crime suspects. They may research developing or improving forensic techniques. Poor 4-5 years Manufacturing and technology, Social and community services $74K-$88K per year, $115K-$166K per year
185 Urban/​Regional Planner Kaiwhakamahere Tāone/​Rohe Urban/regional planners develop and administer plans for physical, environmental, social and economic development of urban and rural areas. Good 3-4 years Social and community services $65K-$130K per year
186 Building Surveyor KairΕ«ri Whare Building surveyors inspect plans and building constructions to see if buildings are, or will be, built correctly. They may also issue certificates, write reports and help owners and potential buyers with construction problems and solutions. Good N/A Construction and infrastructure, Social and community services $61K-$112K per year
187 Interior Designer Kaitātai o-Roto Interior designers plan, design, decorate and furnish spaces in residential, commercial, retail and leisure environments. Average 3 years Construction and infrastructure, Creative industries $55K-$95K per year, $90K-$120K per year
188 Media Producer Kaihautū Pāpāho Media producers plan and produce films, television programmes, theatre productions, music, digital content, radio shows, festivals and other artistic activities. Poor N/A Creative industries
189 Caretaker Kaitautiaki Caretakers keep places such as schools, apartment blocks and public buildings clean, safe and in good order. Poor N/A Services industries, Construction and infrastructure $23-$26 per hour
190 Property Manager Kaiwhakahaere Papa Whenua Property managers look after the daily running of residential or commercial properties. Good N/A Construction and infrastructure, Services industries $56K-$82K per year, $82K-$122K per year
191 Zoologist KaipΕ«taiao Kararehe Zoologists study animals and their behaviour in the wild or in captivity, and how they interact with other species and their environments. Average 3 years Social and community services, Primary industries $57K-$142K per year
192 Case Manager Kaiwhakahaere KΔ“hi Case managers work with individuals and families to help them overcome hardship, and access social services and support. Good <1 year Social and community services $60K-$80K per year
193 Model Kaiwhakakite Kākahu Models display and promote clothes or other goods on television, online, in magazines and advertising, and on catwalks at fashion shows. Poor N/A Services industries, Creative industries
194 Managing Director/​Chief Executive KaihautΕ« Whakahaere/​Tumu Matua Managing directors/chief executivesΒ lead and make overall decisions for an organisation to make sure it operates effectively. Good N/A Services industries $200K-$800K per year, $300K-$1.2M per year
195 Support Worker Kaiāwhina Tiaki Tangata Support workers help people with health problems or disabilities to do daily tasks, such as housework, and be as independent as possible. Good N/A Social and community services $23-$28 per hour
196 Radiation Therapist Kaihaumanu PΕ«hihi Radiation therapists are part of a specialised team that uses radiation to treat cancer and other diseases Average 3 years Social and community services $66K-$98K per year, $101K-$124K per year
197 Entertainer Kaiwhakangahau Entertainers perform a variety of acts, such as dance, drama or acrobatics, to entertain an audience. Poor N/A Creative industries
198 Film/​Television Camera Operator Kaitango Whakaahua Whitiāhua/​Pouaka Whakaata Film and television camera operators use digital and film cameras to record events and scenes for television, movies and videos. Poor N/A Creative industries $30-$50 per hour, $64-$120 per hour
199 Insurance Adviser Kaitohutohu Inihua Insurance advisers give advice about insurance and sell insurance to clients. Good N/A Services industries $60K-$100K per year, $70K-$130K per year
200 Rubbish/​Recycling Collector Kaiwhakahiato Rāpihi Rubbish/recycling collectors collect household, industrial or commercial rubbish for disposal or recycling. Average N/A Construction and infrastructure $23 per hour
201 Health Services Manager Kaiwhakahaere Ratonga Hauora Health services managers are responsible for the day-to-day running of a hospital, primary health organisation (PHO), clinic or community health service. Good 3-5 years Social and community services $65K-$125K per year, $125K-$245K per year
202 Painter and Decorator Kaipeita/​Kaiwhakapaipai Whare Painters and decorators apply decorative and protective finishes to interior and exterior walls, doors, windows and other surfaces of buildings. Good 3 years Construction and infrastructure $53K-$76K per year
203 Customs Officer Δ€piha Taupare Customs officers control the entry and departure of goods, ships, planes and people to and from New Zealand. Good <1 year Social and community services $54K-$73K per year, $98K-$125K per year
204 Marketing Specialist Ngaio Whakatairanga Marketing specialists develop and implement plans for promoting an organisation's goods, services and ideas. Average 3 years Services industries $55K-$90K per year, $100K-$170K per year
205 Dietitian Pūkenga Whakaita Kai (Ngā Tohunga Mātai Kai) Dietitians provide advice and counselling about diet, food and nutrition to individuals and communities. They also design nutrition programmes to support health and wellbeing. Average 5 years Services industries, Social and community services $59K-$86K per year, $88K-$119K per year
206 Agricultural/​Horticultural Consultant Kaitohutohu Ahuwhenua Agricultural/horticultural consultants advise farmers, growers and organisations on business, production and land management solutions. Good 3 years Primary industries $50K-$85K per year, $85K-$150K per year
207 Management Consultant Kaitohutohu Whakahaere Management consultants work with organisations to solve problems and recommend improvements to strengthen business performance. Average 3 years Services industries $61K-$71K per year, $66K-$102K per year
208 Earthmoving Machine Operator Kaiwhakamahi Wakapana Oneone Earthmoving machine operators use digging machines, such as bulldozers or graders, to move, shape or level earth, rock and rubble. Good <1 year Construction and infrastructure $25-$40 per hour
209 Bank Worker Kaimahi Whare PΕ«tea Bank workers receive deposits and pay out money, keep records of transactions, issue receipts, and advise customers on banking services and products. Average N/A Services industries $55K-$80K per year, $70K-$145K per year
210 Actor Kaitapere Actors entertain people by using body movement and speech to play a character in media and stage productions. Poor N/A Creative industries
211 Purchasing/​Supply Officer Δ€piha Hoko Purchasing/supply officers buy or supply equipment, materials and services at the best price and quality for an organisation. Good N/A Manufacturing and technology, Services industries $55K-$100K per year, $75K-$170K per year
212 Chemical Production Operator Kaiwhakamahi Whakaputa MatΕ« Chemical production operators perform a variety of tasks involved in producing toiletries or pharmaceutical products, including ointments, creams, aerosols, tablets, capsules, bandages and vaccines. Poor N/A Manufacturing and technology $23-$28 per hour
213 Food and Beverage Factory Worker Kaimahi TohitΕ« Whakanao Kai/​Inu Food and beverage factory workers prepare ingredients, operate machinery, and bottle or package food and drink. Average N/A Manufacturing and technology $23-$25 per hour
214 Packhouse Worker Kaimahi Whare Putunga Packhouse workers grade, pack and store fruit, vegetables and other produce in packhouses. Good N/A Primary industries $23-$25 per hour
215 Production Manager Kaiwhakahaere Whakaputa Production managers organise and control the production process in a factory. They ensure that products are made to the right specifications and are ready on time and within budget. Average 1-3 years Manufacturing and technology $80K-$150K per year
216 Food Technologist Kaihangarau Kai Food technologists research, develop and improve food andΒ drink products and their processing, packaging, storage and safety. Good 3-5 years Manufacturing and technology, Primary industries $50K-$75K per year, $100K-$140K per year
217 Finance Manager Kaiwhakahaere PΕ«tea Finance managers oversee the major financial operations of an organisation. Good 3 years Services industries $87K to $117K per year, $110K-$170K per year
218 Health and Safety Adviser Kaitohutohu Hauora-Haumaru Health and safety advisers monitor workplace health and safety hazards, train employees on health and safety procedures, and investigate accidents. Good 1-3 years Services industries $51K-$70K per year, $100K-$125K per year
219 Dancer Kaikanikani Dancers entertain people by expressing ideas and emotions, usually to music, using body movements. Poor N/A Creative industries
220 Surveyor KairΕ«ri Surveyors plan, direct and conduct survey work to determine the position of boundaries, locations, topographic features and built structures. Good 4 years Construction and infrastructure $90K-$110K per year
221 Intelligence Officer Δ€piha Matataua Intelligence officers collect and analyse information on people, places and events that may be a threat to businesses or national and international security. Good 1-3 years Services industries, Social and community services $71K-$86K per year, $83K-$170K per year
222 Welder Kaihonohono Maitai Welders make, join and repair metal parts for machinery and equipment using welding techniques. Good 1-3 years Manufacturing and technology, Construction and infrastructure $60-$65 per year, $105K-$125K per year
223 Accountant Kaikaute PΕ«tea Accountants provide accounting services to companies, organisations and individuals. They prepare financial statements and forms, and advise clients on financial aspects of business. Good 2-6 years Services industries $70K per year, $165K per year
224 Physiotherapist Kairomiromi Physiotherapists help people regain movement and function after they have been affected by an injury, disability or health condition. They also give advice on how to prevent injuries. Good 4 years Social and community services $58K-$86K per year, $87K-$119K per year
225 Community Karitāne Karitāne – Hapori Whānui Community karitāne offer support to families with children under the age of five, and provide information on parenting issues such as breastfeeding, infant nutrition, sleeping and child behaviour. Poor 1 year Social and community services $68k-$83K per year
226 Health Care Assistant Kaiāwhina Haumanu Hauora/​Kaimahi Atawhai Health care assistants care for people in aged residential care, private homes, hospitals and disability support. Good 1-2 years Social and community services $23-$34 per hour
227 Registered Nurse Tapuhi Whai RΔ“hitatanga Registered nurses assess, treat and support people who are sick, disabled or injured, in hospitals, clinics, rest homes, and nursing homes. Good 3 years Social and community services $68K-$84K per year, $74K-$153K per year
228 Solicitor Rōia Solicitors give legal advice, prepare legal documents and study the details of legal arguments. Poor 3-4 years Services industries $62K per year, $131K per year
229 Barrister Rōia Kōti Barristers give legal advice and appear on behalf of clients in civil, family and criminal cases in courts and tribunals. Average 3-4 years Services industries $63K per year, $131K per year
230 Plumber, Gasfitter and Drainlayer Kaiwhakarerewai, KaiwhakarerekorohΕ«, Kaiwhakatakoto Paipa Wai Plumbers, gasfitters and drainlayers assemble, install and repair pipes, drains and fixtures and fittings that supply water and gas or remove waste. Good 2-4 years Construction and infrastructure $23-$53 per hour
231 Roading Construction Worker Kaimahi Hanga Rori Roading construction workers make surfaces such as roads, airport runways and driveways, and control traffic around road construction sites. Good N/A Construction and infrastructure $23-$24 per hour, $85,000 a year.
232 Building and Construction Labourer Kaimahi Whaihanga Building and construction labourers do a wide range of physical work on building sites, roads and other large civil construction projects. Good N/A Construction and infrastructure, Primary industries $23-$33 per hour, $25-$40 per hour
233 Biomedical Engineer Mataaro Rongoā Koiora Biomedical engineers design, build and maintain medical equipment, artificial body parts and computer programs to help treat disabilities, diseases, or injuries. Good 4 years Social and community services, Manufacturing and technology $50K-$55K per year, $110K-$130K per year
234 Health Promoter Kaiwhakatairanga Hauora Health promoters work with communities and groups to develop ways to improve people’s health. They also work with government agencies to improve environmental conditions. Average 1-3 years Social and community services $58K-$86K per year, $87K-$119K per year
235 Radiation Oncologist Kaimātai Mate Pukupuku Radiation oncologists provide radiation treatment and management of patients with cancer and other medical conditions. Good 13 years Social and community services $64K-$205K per year, $170K-$251K per year
236 Economist Ngaio Ōhanga Economists analyse financial, labour and trade markets, and predict or explain economic events. Poor 3-5 years Services industries $80K-$125K per year
237 Court Registry Officer Δ€piha Whakarite Kōti/​Ture Court registry officers assist with the day-to-day operation of courts. They handle court documents, schedules and may support the judge in running court hearings. Average N/A Social and community services $59K-$81K per year
238 Beauty Therapist Kaihaumanu Kanohi Beauty therapists provide treatments such as facials, massage, laser hair removal, waxing and pedicures. Average 1-2 years Services industries, Creative industries $50K-$70K per year
239 Sales Representative Kanohi Hokohoko Sales representatives promote, market and sell products or services to business and professional establishments, or wholesale or retail outlets. Average N/A Services industries $50K-$85K per year, $100K or more per year
240 Oral Health Therapist Kaiakuaku Waha Oral health therapists provide dental care, treat gum disease andΒ teach people how to care for teeth and gums. They may refer clients to dentists. Good 3 years Social and community services $59K-$83K per year, $87K-$119K per year
241 Film and Video Editor Δ’tita Kiriata/​Ētita Ataata Film and video editors assembleΒ video, graphics, audio and text into a finished product for films, television programs, video productions or commercials. Poor N/A Creative industries $27 per hour
242 Production Assistant (Film, Television, Radio or Stage) Kaiāwhina Whakarite (Kiriata, Pouaka Whakaata, Irirangi, Whakaari rānei) Production assistants help production teams organise the making of film, television, radio or stage productions. Poor N/A Services industries, Creative industries
243 Musician Kaiwhakatangitangi Musicians write, arrange, conduct, and perform musical compositions. Poor N/A Creative industries
244 Industrial Designer Kaitātai Ahumahi Industrial designers design and develop innovative products for use in homes, businesses and industry. Average 3 years Manufacturing and technology, Services industries, Creative industries $60K-$75K per year, $90K-$120K per year
245 Psychiatrist Rata Mate Hinengaro Psychiatrists diagnose and treat mental illness and emotional and behavioural disorders by providing psychotherapeutic treatment and psychiatric medication. Good 12 years Social and community services $64K-$205K per year, $170K-$251K per year
246 Funeral Director/​Embalmer KaihautΕ«/​Kaiwhakapaipai TΕ«pāpaku Funeral directors/embalmers organise and direct funerals, register deaths, and prepare human bodies for visits by families, and for burial or cremation. Poor 1-2 years Services industries $50K-$60K per year
247 Speech-Language Therapist Kaihaumanu Reo ā-Waha Speech-language therapists assess and treat people who have problems with verbal communication or swallowing. This may include difficulties with speech, language, listening, reading or writing. Good 4-5 years Social and community services $55K-$119K per year
248 Firefighter Kaitinei Ahi FirefightersΒ control and put out fires, help rescue people and animals, andΒ educate the public about fire safety and fire prevention. Poor <1 year Social and community services $54K-$77K per year, $88K-$108K per year
249 Medical Physicist KaiahupΕ«ngao Whakaora Medical physicists help plan radiation treatment for patients, check and monitor radiation equipment, and develop new treatment techniques. Good 8 years Services industries $65K-$118K per year, $122K-$161K per year
250 Quarantine Officer Δ€piha Δ€rai Mate Hōrapa Quarantine officers identify and control biosecurity risks at New Zealand's borders by inspecting goods and vessels arriving in the country. Average N/A Social and community services, Primary industries $65K-$71K per year, $75K-$80K per year
251 Lift Technician Kaihangarau Waka Kawe Lift technicians install, maintain and repair lift and escalator systems. Average 3-4 years Manufacturing and technology, Construction and infrastructure $55K-$75K per year, $75K-$100K per year
252 Table Games Dealer Kaimahi Wharepeti Table games dealers lead and control games played at casinos, calculate winnings and losses, and pay out winning bets. Average <1 year Services industries
253 Massage Therapist Kaihaumanu Mirimiri Massage therapists manipulate the soft tissue of people's bodies to treat health problems and to help people relax. Average N/A Services industries
254 Landscaper Kaihoahoa Whakapaipai Whenua Landscapers design, develop, maintain and remodel gardens and landscapes. Average N/A Construction and infrastructure, Primary industries $23 per hour, $25-$36 per hour
255 Stonemason Pouwhakanao Kōhatu Stonemasons work with stone to construct or renovate buildings, fittings, walls and paving, or to create monuments. Good 2-3 years Construction and infrastructure, Creative industries $23-$40 per hour
256 Actuary Kaitauwhiro PΕ«tea Actuaries predict and assess the financial risks and impacts of future events. They work in areas such as insurance, superannuation and investment. Good 5-8 years Services industries $55K-$165K per year, $140K-$180K per year
257 Marine Engineer Mataaro Kaipuke Marine engineers operate, service and repair engines, and mechanical and electronic equipment on ships and boats. Poor 1-3 years Manufacturing and technology $65K-$80K per year, $80K-$184K per year
258 Fabrication Engineer Mataaro Piharoa Fabrication engineers make, install and repair metal products such as vents, handrails, boilers, aircraft and boat parts, or beams and girders for construction projects. Average 1-3 years Construction and infrastructure, Manufacturing and technology $60K-$70K per year, $80K or more per year
259 Sterilising Technician Kaihangarau Whakahoromata Sterilising technicians clean, sterilise and package surgical instruments and other hospital equipment, soft goods and linen in a sterilisation unit. Good 2 years Social and community services $52K - $62K per year, $66K-$75K per year
260 Art Director (Film, Television or Stage) Kaitohu Toi (Kiriata, Pouaka Whakaata, Whakaari rānei) Art directors plan, organise and control artistic aspects of film, television or stage productions. Poor N/A Creative industries
261 Clinical Coder Kaiwhakararangi Tohu Hauora Clinical coders convert information in patient discharge notes into health classification codes. This information is used for research and to plan health funding and services. Good <1 year Social and community services $76K-$88K per year, $93K per year
262 Joiner Kaihanga Taonga ā-Whare Joiners use timber and board products to make fittings such as cabinets, doors, window frames and stairs. Good 3-4 years Construction and infrastructure, Manufacturing and technology $23-$25 per hour, $30-$35 per hour
263 Collision Repair Technician Kaihangarau Tinana Waka Collision repair technicians repair and replace damaged body parts of cars and other vehicles. Good 3-4 years Manufacturing and technology, Services industries $60-$75 per year
264 Diversional and Recreational Therapist Kaihaumanu RΔ“hia Diversional and recreational therapists design and run recreation and leisure programmes to support and enhance people's total wellbeing. Good N/A Services industries, Social and community services $28-$35 per hour, $36-$50 per hour
265 Occupational Therapist Kaiwhakaora Ngangahau Occupational therapists provide therapy and support to people with limited ability to carry out everyday activities because of illness, injury or disability. Average 3 years Social and community services $58K-$119K per year
266 Elected Government Representative Māngai ā-Pōtitanga Elected government representatives are elected by the people of a specific area to help govern a city, region or country. Poor N/A Social and community services $160K-$300K per year, $2K-$190K per year
267 Window Cleaner Kaihoroi Matapihi Window cleaners clean windows and other glass in shops, schools, offices, hospitals and homes. Good N/A Services industries $23 per hour, $23-$35 per hour
268 Health and Safety Inspector Kaimātai Hauora-Haumaru Health and safety inspectors assess workplaces and work activities to determine if employers are keeping workers and other people safe and healthy at work. They also educate people about health and safety, investigate accidents and lead prosecutions. Poor 1-2 years Services industries, Social and community services $61K-$66K per year, $76K-$93K per year
269 Career Consultant Kaitohutohu Umanga Career consultants help clients with career decision-making and development, job hunting, and returning to work after illness or accident. Poor 2-5 years Social and community services $51K-$103K per year
270 Surgeon Rata Hāparapara Surgeons consult with patients and operate on people to treat and manage disease and injuries. Good 13-15 years Social and community services $64K-$205K per year, $170K-$251K per year
271 Pathologist Kaimātai Mate Tangata Pathologists are doctors who diagnose and study human diseases and conditions. They diagnose health problems by testing tissue and fluid samples taken from patients. Good 13-15 years Social and community services $64K-$205K per year, $170K-$251K per year
272 Business Analyst Kaitātari Pakihi Business analysts design or recommend solutions, such as computers or computer programs, to help organisations meet their goals. Good 2-3 years Manufacturing and technology $115K–$140K per year, $145K–$160K per year
273 Information Technology Helpdesk/​Support Technician Kaihangarau Δ€whina Hangarau Pārongo Information technology (IT) helpdesk/support technicians set up computer and IT equipment and identify and fix hardware and software problems Good 1-3 years Manufacturing and technology $60K-$90K per year, $90K-$120K per year
274 Immigration Officer Δ€piha Whakahaere Manene Immigration officers control the entry of people from other countries into New Zealand, assessing visa applications from people who wish to visit, study, work or reside in NZ. Good <1 year Social and community services $63K per year, $91K or more per year
275 Security Analyst Kaitātari Whakamarumaru Security analysts create and monitor security processes and frameworks to protect an organisations information systems and computer networks from being illegally accessed. Good 1-4 years Manufacturing and technology $120K-$200K per year, $150K-$500K per year
276 Security Consultant Kaitohutohu Whakamarumaru Security consultants identify security weakness in information technology (IT), advise on IT security, and design IT security systems. Good 2-3 years Manufacturing and technology $120K-$200K per year, $180K-$500K per year
277 Test Analyst Kaitātari Whakamātautau Test analysts design and carry out tests for computer software and systems, analyse results, and identify and report problems. Good 1-3 years Manufacturing and technology $80K-$130K per year, $130K-$200K per year
278 Scrum Master Kaitakawaenga Kakari Scrum masters use the scrum software development approach to keep IT project teams on track. They also help remove obstacles to progress. Good 2-3 years Manufacturing and technology $120K-$200K per year
279 Penetration Tester Kaiwhakamātautau Mūrere Penetration testers investigate security weaknesses in online systems and databases. Good 1-4 years Manufacturing and technology $100K-$200K per year
280 Emergency Management Officer Δ€piha Whakahaere Ohotata Emergency management officers plan for and respond to emergencies such as earthquakes and weather events. They also train communities to prepare for disasters. Average >1 year Social and community services $66K-$79K per year, $79K-$95K per year
281 Pharmacy Technician Kaihangarau Rongoā Pharmacy technicians help pharmacists to prepare and give out medicines. Good 2 years Services industries, Social and community services $52K-$80K per year
282 Architect Kaihoahoa Whare Architects plan, design and advise on the construction and alteration of buildings and other structures. Average 8-10 years Construction and infrastructure, Manufacturing and technology, Creative industries $60K-$90K per year, $110K-$140K per year
283 Architectural Technician Kaihangarau Hoahoa Whare Architectural technicians create drawings and make models of building structures, research construction materials, and assist with building consent processes. Average 2 years Construction and infrastructure, Manufacturing and technology, Creative industries $70K-$125K per year
284 Receptionist Kaiwhakatau Manuhiri Receptionists greet visitors and clients, and deal with enquiries and requests. Their work includes administration tasks such as answering the telephone, scheduling appointments and keeping records. Average N/A Services industries $48K-$65K per year
285 Data Entry Operator/​Transcriptionist Kaiwhakauru Raraunga/​Kaipatopato Kōrero Data entry operators/transcriptionistsΒ copy or transcribe information that is spoken or written. Average N/A Services industries $48K-$55K per year
286 Game Developer Kaihanga Tākaro Game developers write, design, program, animate and test games for computers, gaming consoles, tablets and mobile phones. Poor 2-3 years Manufacturing and technology, Creative industries $60K-$80K per year, $80K-$100K per year
287 Software Developer Kaihanga PΕ«manawa Rorohiko Software developers create and maintain computer software, websites and software applications (apps). Good 1-3 years Manufacturing and technology $110K- $160K per year
288 Electrical Engineering Technician Kaihangarau Take PΕ«hiko Electrical engineering technicians interpret the designs and technical instructions of electrical engineers, then ensure they are accurately carried out. They also develop, install, test and maintain equipment that produces, transmits or uses power. Good 2-3 years Manufacturing and technology $23-$27 per hour, $34-$43 per hour
289 Electrician Kaimahi Hiko Electricians test, install, maintain and repair electrical systems and equipment. Good 3-4 years Construction and infrastructure, Manufacturing and technology $34 per hour, $43 per hour
290 Refrigeration/​Air-conditioning Technician Kaihangarau PΕ«rere Whakamātao Refrigeration/air-conditioning technicians install, service and maintain refrigeration and air-conditioning systems. Good 0-4 years Manufacturing and technology $50K-$95K per year
291 Social Worker Kaimahi Toko i te Ora Social workers provide care, advice and support to people with personal or social problems, and help with community and social issues. Average 4-6 years Social and community services $59K-$75K per year, $75K-$118K per year
292 Biomedical Technician Kaihangarau Utauta Whakaora Biomedical technicians make, modify, maintain and repair mechanical and electronic medical equipment such as clinical machines, surgical instruments and implants. Good 2 years Manufacturing and technology, Social and community services $58K-$86K per year, $87K-$104K per year
293 Tour Guide Kaiārahi Rōpū Haere Tour guides escort people on sightseeing, educational or other tours, and describe points of interest. Poor N/A Services industries
294 Advertising Specialist Ngaio Pānui Toko Advertising specialists create, co-ordinate, plan and implement advertising campaigns to sell products or services for clients. Average N/A Services industries, Creative industries $48K-$150K per year
295 Copywriter Kaituhi Pānui Copywriters design and create print, digital, social media, video, television and radio advertisements. Poor N/A Creative industries $47K-$60K per year, $100K-$160K per year
296 Graphic Pre-press Worker Kaimahi Whakairoiro (Mua Perehi) Graphic pre-press workers use computers to prepare text and designs for printing. Poor 3 years Manufacturing and technology, Creative industries $47K-$50K per year, $60K-$80K per year
297 Animator/​Digital Artist Kaiwhakahauora/​Ringa Toi Mamati Animators and digital artists use software, models, photography and drawings to create still and moving images for advertisements, film, print, web or television. Average 3 years Creative industries $55K-$100K per year, $48k-$120K per year
298 Artist Kaitoi Artists turn creative ideas into works of art using media such as paint, digital resources, fabric and feathers, clay, stone and wood. Poor N/A Creative industries
299 Graphic Designer Kaihoahoa Whakairoiro Graphic designers create artwork or designs for printed and electronic media such as magazines, television and websites. Average 3 years Services industries, Creative industries $65K-$105K per year
300 Aeroplane Pilot Kaiwhakahaere Waka Rererangi Aeroplane pilots fly planes that transport people and goods, or spread fertiliser or bait. Good 2-3 years Services industries , $48K-$148K per year
301 Flight Instructor Kaiwhakaako Waka Rererangi FlightΒ instructors teach people how to fly aeroplanes, helicopters or other aircraft. Good 2-3 years Services industries $47K-$70K per year, $70K-$100K per year
302 Aircraft Maintenance Engineer Mataaro Whakatika Waka Rererangi Aircraft maintenance engineers keep aeroplanes safe. They install, inspect, maintain and repair aircraft, aircraft radio, avionic (electronic), navigation, communication and electrical and mechanical systems. Good 3-5 years Manufacturing and technology $48K-$91K per year, $86K-$165K per year
303 Army Officer Δ€piha Ope Tauā Army officers train army soldiers, manage field exercises and lead soldiers in combat, security operations, peacekeeping missions and disaster relief. Average 1-2 years Services industries, Social and community services $51K-$74K per year, $101K-$133K per year
304 Army Soldier Hōia Ope Tauā Army soldiers defend their country, forming highly skilled teams that work together in combat and security operations, peacekeeping missions, humanitarian assistance and disaster relief. Good <1 year Services industries, Social and community services $51K per year, $101K-$120K per year
305 Beekeeper Kaitiaki PΔ« Beekeepers look after beehives in apiaries that produce honey, wax, pollen and other bee products. They may also offer pollination services to horticultural and seed crop producers. Average 1-2 years Primary industries $48K-$70K per year, $65K-$120K per year
306 Audiologist/​Audiometrist Kaimātai Ororongo/​Kaimātau Ororongo Audiologists and audiometrists study, identify, measure and treat hearing loss and ear disorders. They also provide aids and other listening devices to assist patients with hearing loss. Good 2-5 years Social and community services $50K-$120K per year, $48K-$75K per year
307 Agricultural Technician Kaihangarau Ahuwhenua Agricultural technicians perform tests and experiments, and provide technical support to assist agricultural scientists in areas such as research, production, servicing and marketing. Average 3 years Primary industries, Services industries $48K-$65K per year, $55K-$85K per year
308 Archivist Kaitiaki PΕ«ranga Archivists assess, organise, store and provide access to records and documents of long-term historical or research value. They also advise people and organisations about their archives. Average 3-5 years Services industries, Creative industries $48K-$60K per year, $60K-$70K per year
309 Chef PΕ«kenga Tao Kai Chefs prepare and cook food in restaurants, hotels, catering businesses, rest homes, cafes and bars. Good 1-3 years Services industries $23-$25 per hour, $29-$38 per hour
310 Cook Ringawera Cooks prepare, cook and serve food. They work in cafes, bars, hospitals, schools, daycare centres, fast food outlets, or for caterers. Good N/A Services industries $25-$27 per hour
311 Kitchenhand Ringawera Kitchenhands wash dishes and clean the kitchen and serving areas in eating places. They may also help kitchen staff prepare food. Good N/A Services industries $23 per hour
312 Biochemist Kairarau MatΕ« Koiora Biochemists study the chemical structure and function of animals, plants and micro-organisms such as bacteria and viruses. They use this research to develop medical, industrial and agricultural products. Average 3-9 years Manufacturing and technology, Primary industries $48K-$75K per year, $76K-$130K per year
313 Biotechnologist Ringa Hangarau Koiora Biotechnologists use their knowledge of living thingsΒ to develop new animal or plant products such as medicines and pest-resistant crops. Good 3-5 years Manufacturing and technology, Primary industries $48K-$75K per year, $76K-$130K per year
314 Courier/​Delivery Agent Kaikawe Karere/​Utanga Couriers/delivery agents sort, collect and deliver mail, packages, parcels and other goods to homes and businesses. Average <1 year Services industries $48K-$130K per year
315 Delivery Driver Kaitaraiwa Waka Whakarato Delivery drivers distribute and may sell products to commercial and home delivery customers. Good N/A Services industries $23-$25 per hour
316 Buyer Kaihoko Buyers purchase goods to sell in warehouses, shops or department stores. Average N/A Services industries $48K-$60K per year, $60K-140K per year
317 Chiropractor Kaikorohiti Chiropractors treat disorders related to the spine and nervous system to relieve pain and improve the function of nerves, muscles and joints. Good 5 years Social and community services $48K-$60K per year, $60K-$150K per year
318 Brewer KaitoroΔ« Brewers use brewing equipment and processesΒ to convert malted barley or other grains into beer, andΒ control or manage the production and packaging of beer. Average N/A Services industries, Manufacturing and technology $48K-$60K per year, $60K-$120K per year
319 Customs Broker/​Freight Forwarder Kaiwhakawhiti Taupare/​Utanga Customs brokers and freight forwarders arrange the clearance (through customs) and collection of imported cargo, and the shipment of cargo for export. Average <1 year Services industries $48K-$90K per year, $48K-$70K per year
320 Cabinet Maker Kaihanga Kapata Taonga Cabinet makers make and repair fittings and furniture for homes, businesses and boats. Good 3-4 years Manufacturing and technology, Construction and infrastructure $48K-$60K per year
321 Chemist Kairarau MatΕ« Chemists study the make-up and behaviour of chemicals, and may use their findings to develop new products and processes. Good 5 years Manufacturing and technology, Services industries $65K-$120K per year, $150K per year
322 Dairy Farmer Kaiahuwhenua Miraka Kau Dairy farmers plan and manage milk production by cows, maintain pasture and monitor environmental impacts on farms. Good N/A Primary industries $55K-$67K per year, $83K-$103K per year
323 Dairy Farm Assistant Kaiāwhina Pāmu Kau Dairy farm assistants help farmers with a variety of tasks, including caring for and milking cows, repairs and maintenance, and other farming activities. Good N/A Primary industries $48K-$80K per year
324 Sharemilker Kaiwhakahaere MΔ«raka Kau Sharemilkers either milk a dairy farmer's cows for a profit share, or own a herd of cows and milk them on an owner's land for a profit share. Good N/A Primary industries $64K-$97K per year
325 Dental Assistant Kaiāwhina Mahi Niho Dental assistants help dentists with patient care and running dental practices. Good N/A Social and community services $48K per year, $51K-$62K per year
326 Dental Technician Kaihangarau Niho Dental technicians create and repair devices for the treatment, replacement and protection of damaged, badly positioned or missing teeth. Good 3 years Social and community services $48K per year, $60K-$75K per year
327 Diver Kairukuruku Commercial divers develop and maintain underwater structures and do research for scientists. Recreational divers teachΒ scuba diving and may carry out marine searches and rescues. Average <1 year Construction and infrastructure, Services industries $48K-$49K per year, $350-$1200 per day
328 Driving Instructor Kaiwhakaako Taraiwa Waka Driving instructors teach people how to drive, and instruct experienced drivers how to advance their driving skills and road safety knowledge. Average <1 year Services industries $48K-$80K per year
329 Deckhand Ringa Paparahi Deckhands may take care of passengers and assist in the operation of vessels such as harbour ferries and charter boats, or cast and haul in nets, lines or pots, and process fish on inshore or deep-sea fishing vessels. Good N/A Services industries $48K-$55K per year, $48K-$90K per year
330 Editor Δ’tita/​Kaiwhakatika Editors plan, commission, evaluate, select, edit and organise material for publication online or in books, magazines, and newspapers. They may also manage editorial staff. Average 1-3 years Creative industries $48K-$60K per year, $60K-$90K per year
331 Dispensing Optician Ngaio Mōhiti Dispensing opticians interpret prescriptions from optometrists and ophthalmologists (eye specialists) for glasses or contact lenses, assemble and fit glasses, and sell customers frames and lenses. Good 1-2 years Social and community services $48K-$60K per year, $60K-$85K per year
332 Debt Collector Kaikohi Nama Debt collectors help businesses and individuals collect money or goods from people with overdue debts. Average N/A Services industries $48K-$65K per year
333 Office Administrator Kaiwhakarite ā-Tari Office Administrators help ensure organisations run efficiently. Average N/A Services industries $48K-$65K per year
334 Office Manager Kaiwhakahaere Tari Office managers are responsible for a range of tasks that make an office run smoothly, including administrative duties, staff supervision and financial work. Average N/A Services industries $60K-$85K per year
335 Event Manager Kaiwhakahaere Tauwhāinga Event managers plan, promote and run events, such as conferences, for a variety of clients. Good N/A Services industries, Creative industries $48K-$50K per year, $50K-$75K per year
336 Maitre d’Hotel Kaiwhakarite Hōtera Maitres d'hotel oversee the service of food and beverages to guests in restaurants and other eating places. They also check reservations, greet guests and supervise the waiting staff. Good N/A Services industries $27 per hour
337 Aquaculture Farmer Kaiahumoana Aquaculture farmers manage the breeding, raising and harvesting of fish and shellfishΒ for commercial purposesΒ in marine or freshwater farms. Good N/A Primary industries $48K-$65K per year, $59K-$88K per year
338 Fishing Skipper Kaiurungi Hao Ika Fishing skippers are responsible for running a fishing boat. Responsibilities range from navigating the vessel and organising the crew to catching and processing fish. Average <2 years Primary industries $48K-$80K per year, $130K-$280K per year
339 Flight Attendant TΕ«mau Waka Rererangi Flight attendants make sure that passengers travelling in aeroplanes are safe and comfortable. Poor <1 year Services industries $48K-$50K per year
340 Fashion Designer Kaihoahoa Kākahu Fashion designers design clothing and accessories. Poor 1-3 years Manufacturing and technology, Creative industries $48K-$50K per year, $50K-$160K per year
341 Technical Writer Kaitito Hangarau Technical writers create content for printed and online media, such as user guides and webpages, and present it in a way that can be easily accessed and understood. Good N/A Manufacturing and technology, Services industries $50K-$60K per year, $60K-$130K per year
342 Engineering Machinist Kaiwhakamahi PΕ«rere Engineering machinists create, assemble and repair metal products by interpreting designs, measuring metals, and operating machines to cut and shape them. Good 4 years Manufacturing and technology $48K per year, $48K-$103K per year
343 Exhibition and Collections Technician Kaihangarau Whakaaturanga/​Kohinga Exhibition and collectionsΒ technicians prepare, install, maintain and dismantle museum and art gallery exhibitions. Poor 1-3 years Services industries, Creative industries $48K-$55K per year
344 Forestry and Logging Worker Kaimahi Waonui/​Tope Rākau Forestry and logging workers plant, maintain, measure, cut and clear trees from forests. Good >1 year Primary industries $48K-$65K per year, $65K-$75K per year
345 Electronics Trades Worker Ringarehe Tāhiko Electronics trades workers assemble, install and fix electronic parts and equipment. Good 3-4 years Manufacturing and technology $48K-$80K per year, $75K-$120K per year
346 Furniture Finisher Kaiwhakaoti Taonga Rākau Furniture finishers prepare the final surface of items of furniture and apply stain, lacquer, paint, oil or wax. Average 3-4 years Manufacturing and technology, Creative industries $48K-$60K per year
347 Cutter Ringa Tapahi Cutters lay out and cut fabric to make clothing and soft furnishings. Average >1 year Manufacturing and technology, Creative industries $23-$26 per hour
348 Garment Technician Kaihangarau PΕ«eru Garment technicians choose clothing fabrics and designs, and make sure clothes are made to quality standards. Average 1 year Manufacturing and technology, Creative industries $48K per year, $48K-$70K per year
349 Sewing Machinist Kaiwhakamahi PΕ«rere Tuitui Sewing machinists stitch together clothing, canvas for tents and awnings, and soft furnishings. Good <1 year Manufacturing and technology, Creative industries $23-$25 per hour
350 Hairdresser/​Barber Kaikutikuti Makawe Hairdressers/barbers cut, colour and style hair. Barbers also shave and trim hair, moustaches and beards. Average 2-4 years Services industries, Creative industries $48K-$55K per year, $65K-$100K per year
351 Geospatial Specialist Ngaio Papa Whenua Geospatial specialists gather and analyse geographic and spatial (location-based) information and use specialist software to present it in user-friendly formats such as maps and 3D models. Good 3 years Services industries, Construction and infrastructure $48K-$65K per year, $70K-$120K per year
352 Historian Pouherenga Kōrero o-Mua Historians research, write and present information about events and people of the past and present. They may also teach history. Poor >5 years Services industries, Creative industries $48K-$120K per year
353 Insurance Claims Officer Δ€piha Tono RΔ«anga Insurance claims officers decide whether an insurance company will pay out a claim. Average N/A Services industries $48K-$75K per year, $60K-$140K per year
354 Jeweller Kaihanga Taonga Rei Jewellers design, make, alter and repair items such as rings, bracelets, necklaces and earrings. Average 4 years Manufacturing and technology, Services industries, Creative industries $48K–$60K per year, $60K–$100K per year
355 Gardener Kaitiaki Māra Gardeners plant and maintain lawns, trees, shrubs and flowers in public or private gardens and parks. Average N/A Primary industries, Services industries $47-$60 per hour
356 Holiday Park Manager Kaiwhakahaere Papa RΔ“hia Holiday park managers run holiday parks, camping grounds, motor camps, caravan parks or seaside resorts as owners or on behalf of owners. Good N/A Services industries $48K-$60K per year
357 Industrial Spray Painter Kairehu Ahumahi Industrial spray painters prepare, paint, powder-coat or resurface industrial parts and machinery and other items used in homes, offices and industries. Average N/A Manufacturing and technology $48K-$62K per year
358 Author Kaituhi Pukapuka Authors write stories, scripts, poems, blogs or plays for publication or production, to entertain and inform people. Poor N/A Creative industries
359 Baker Kaitunu Parāoa Bakers prepare, bake and decorate bread, rolls, pastries, desserts, cakes and slices. Good 2-3 years Services industries, Manufacturing and technology $23-$30 per hour
360 Journalist KairΔ«poata Pepa Journalists research and produce stories for websites, print, radio, television and other media. Poor 1-3 years Services industries, Creative industries $48K-$100K per year
361 Communications Professional Ngaio Whakawhitiwhiti Kōrero Communications professionals develop strategies to promote the image of organisations to the public, shareholders and employees. Good 1-3 years Services industries, Creative industries $110K-$130K per year, $130K-$180K per year
362 Mechanical Engineering Technician Kaihangarau Take PΕ«kaha Mechanical engineering technicians help mechanical engineers design, develop, test and manufacture mechanical devices, including tools, engines and machines. Good 2-4 years Manufacturing and technology $48K-$65K per year, $85K-$140K per year
363 Market Research Analyst Kaitātari Rangahau Hokohoko Market research analysts collect and analyse data and information, write reports, and make recommendations to their clients based on their research. Average 3 years Services industries $48K-$70K per year, $70K-$125K per year
364 Survey Interviewer Kaiuiui Rangahautanga Survey interviewers collect facts and opinions by interviewing people. They conduct interviews for market research companies, government agencies and other organisations. Average N/A Services industries $23-$40 per hour
365 Telemarketer Kaihoko ā-Waea Telemarketers promote and sell goods or services by telephone. Poor N/A Services industries $49K-$58K per year
366 Librarian Kaitiaki Pātaka Pukapuka Librarians identify information that people need, organise it and make sure people can access it. Good 3-4 years Social and community services $50K-$75K per year, $60K-$125K per year
367 Library Assistant Kaiāwhina Tiaki Pukapuka Library assistants do a variety of tasks within a library including organising material and helping library users. Average N/A Social and community services $48K-$55K per year, $55K-$65K per year
368 Microbiologist Kaimātai Koiora Mororiki Microbiologists study micro-organisms, such as bacteria, viruses and fungi, and the effects they have on plants, animals and humans. They also develop products from micro-organisms to benefit humans or the environment. Good 3-9 years Primary industries $48K-$75K per year, $75K-$130K per year
369 Motor Vehicle Salesperson Kaihoko Waka Huarahi Motor vehicle salespeople sell new or used cars, trucks, and other vehicles. Good N/A Services industries $48K-$50K per year, $50K-$220K per year
370 Meat/​Seafood Process Worker Kaimahi MΔ«ti/​Mātaitai Meat/seafood process workers process, grade and package meat, fish or shellfish for local and overseas markets. Some also slaughter animals. Good N/A Primary industries, Manufacturing and technology $48K-$86K per year
371 Legal Executive Δ€piha Mahi Ture Legal executives help lawyers prepare and file legal documents, research and prepare cases, give legal advice and help with house sales. Average 2 years Services industries $48K-$70K per year
372 Personal Assistant Kaiāwhina Whaiaro Personal assistants provide administrative, clerical, secretarial and general support to managers and other professionals. They may also be responsible for financial planning, recruitment and staffing. Poor N/A Services industries $55K-$1100K per year
373 Meteorologist Matapae Huarere Meteorologists study the atmosphere to understand and predict weather and climate. Average 4-6 years Services industries $48K-$60K per year, $60K-$165K per year
374 Line Mechanic Kaimahi Waea Kawe Kōrero Line mechanics install, repair and maintain overhead and underground power lines. Good 2 years Construction and infrastructure, Manufacturing and technology $48K-$60K per year, $60K-$100K per year
375 Civil Engineer Mataaro Metarahi Civil engineers plan, organise and oversee the building and maintenance of structures such as dams, bridges, sewerage systems and roads. Good 4 years Construction and infrastructure, Manufacturing and technology $90K-$110K per year
376 Quantity Surveyor KairΕ«ri Utu Hanga Whare Quantity surveyors manage finances for constructions, calculate budgets and prepare detailed estimates to ensure budgets are sufficient. Good 2-3 years Construction and infrastructure $48K-$100K per year, $100K-$170K per year
377 Nanny/​Child Carer Kaitiaki Tamariki Nannies/child carers are responsible for the care, wellbeing and education of infants, toddlers and children in the home. Average N/A Social and community services $23-$30 per hour
378 Outdoor Recreation Guide/​Instructor Kaiārahi o Waho/​Kaiwhakaako o Waho Outdoor recreation guides and instructors teach or guide outdoor activities such as rafting, kayaking, canyoning, skiing, hunting, climbing, caving and mountain biking. Poor N/A Services industries $48K-$58K per year
379 Printer Kaiwhakamahi Mīhini Tā Printers set up and operate printing machines that print text and images on paper, card, carton, plastics or metal. Good 2-3 years Manufacturing and technology $48K-$50K per year, $62K-$93K per year
380 Print Finisher Kaiwhakatau Tānga Print finishers bind, finish and repair books and other publications by hand or by machine. Good 3 years Manufacturing and technology $50K-$65K per year
381 Accounts Officer Kaituhi Kaute Accounts officers are responsible for monitoring and managing financial accounts for their organisation. Good N/A Services industries $48K-$82K per year
382 Payroll Officer Δ€piha Utu Kaimahi Payroll officers arrange payment of staff salaries and wages. Good N/A Services industries $48K-$60K per year, $65K-$130K per year
383 Osteopath Kaiwhakamaimoa Kōiwi Osteopaths diagnose muscular and skeletal injuries and treat them using manual techniques such as stretching, massage and manipulation. Good 4-5 years Social and community services $48K-$60K per year, $60K-$100K per year
384 Product Assembler Kaiwaihanga Taputapu Product assemblers put together parts of metal products, machinery, electrical and electronic equipment, telecommunications equipment, joinery products and other goods. Poor N/A Manufacturing and technology $48K-$50K per year
385 Radio Presenter Māngai Reo Irirangi Radio presenters prepare and present news, music, interviews and other radio programmes to entertain and inform audiences. Poor N/A Creative industries $48K-$70K per year, $70K-$90K per year
386 Pulp and Paper Mill Operator Kaiwhakamahi Mīra Puru Rākau Pulp and paper mill operators operate, maintain and repair machines that make pulp and paper. Poor 1-2 years Manufacturing and technology, Primary industries $48K-$50K per year, $50K-$100K per year
387 Human Resources Adviser Kaitohutohu PΕ«manawa Tangata Human resources advisers are responsible for recruitment advice, performance management and pay, wellbeing, training and development, employment relations and policies for the staff of an organisation. Average 1-3 years Services industries $70K-$115K per year, $95K-$200K per year
388 Recruitment Consultant Kaitohutohu Whiwhi Kaimahi Recruitment consultants help match people with jobs. They work with candidates (people looking for work) and clients (employers looking for staff). Average N/A Services industries $48K-$90K per year, $80K-$130K per year
389 Retail Manager Kaiwhakahaere Hokohoko Retail managersΒ organise and manage the operations of retail stores or departments, including staff. Average N/A Services industries $48K per year, $48K-$60K per year
390 Retail Sales Assistant Kaiāwhina Hokohoko Retail sales assistants serve customers, look after stock items and process sales. Average N/A Services industries $48K per year
391 Ship's Officer Δ€piha Kaipuke Ship's officers navigate and control the safe operation of a ship and supervise and co-ordinate the activities of deck crew. Good 2 years Services industries $60K-$130K per year
392 Ship's Master Poutikanga Whakahaere Kaipuke Ship's masters are in charge of a ship, its crew and any passengers or cargo it is carrying – on the water and in port. On tugs or pilot boats, ship's masters may guide or assist ships in and out of harbours or through difficult waterways. Good 2-3 years Services industries $48K-$70K per year, $125K-$250K per year
393 Sports Coach/​Official Pouako/​Kātipa Hākinakina Sports coaches/officials coach and instruct athletes, and are the officials in charge of sporting events. Poor >1 year Services industries $48K-$80K per year
394 Sound Technician Kaihangarau Oro Sound technicians operate and maintain the equipment used to record, mix and amplify sound for radio, film, television, music and events. Poor N/A Creative industries $48K-$80K per year
395 Recreation Co-ordinator Kaiwhakarite Hākinakina Recreation co-ordinators plan and run community leisure, before and after-school and sport programmes at venues such as recreation centres, parks, clubs and schools. Good N/A Services industries, Social and community services $48K-$57K per year, $60K-$83K per year
396 Shearer Kaikuti Hipi Shearers cut the wool from sheep with clippers. Average N/A Primary industries $48K-$65K per year, $65K-$130K per year
397 Signmaker Kaihanga Tohu Signmakers design, print and install signs in a range of materials, for indoor and outdoor use. Average 4 years Services industries, Creative industries $48K-$70K per year
398 Saw Doctor Kaiwhakatika Kani Saw doctors set and adjust sawmill saws and repair, grind and sharpen hand, band and circular saws. Good 2-3 years Manufacturing and technology, Primary industries $48K-$65K per year
399 Demonstrator Kaiwhakaatu Demonstrators show and explain goods and services to potential customers, and promote new lines of products and services. Good N/A Services industries $23-$28 per hour
400 Visual Merchandiser Kaiwhakatū Taonga ā-Matapihi Visual merchandisers arrange goods and make displays in shops, shop windows and at events to attract the attention of customers. Average N/A Services industries, Creative industries $48K-$55K per year, $55K-$200K per year
401 Water/​Wastewater Treatment Operator Kaiwhakamahi Whakapai Wai (Paru) Water/wastewater treatment operators run water and wastewater treatment plants.Β They treat water so that it is acceptable to drink. They also control the disposal of sewage and industrial wastewater. Good 2 years Construction and infrastructure, Manufacturing and technology $48K-$70K per year, $50K-$130K per year
402 Winemaker Kaihanga Waina Winemakers make wine from grapes and other fruit. Average N/A Manufacturing and technology, Primary industries $48K to $83K per year, $89K-$211K per year
403 Stevedore Kaimahi Taunga Waka Stevedores operate a variety of heavy machinery to load, unload, tally and stow the cargo of a ship. Average 1 year Services industries $48K-$80K per year, $80K-$130K per year
404 Viticulturist Kaiparuauru Kerepe Viticulturists grow and harvest grapes from grapevines, and manage vineyards. Good N/A Primary industries $48K-$53K per year, $63K-$128K per year
405 Survey Technician Kaihangarau RΕ«ri Whenua Survey technicians collect, record, and evaluate geographical information and prepareΒ databases, maps, charts and plans. Good 2 years Construction and infrastructure $48K-$55K per year, $60K-$80K per year
406 Travel Agent/​Adviser Māngai Whakarite Haerenga/​Kaitohutohu Whakarite Haerenga Travel agents/advisers provide information about tourism attractions, sell travel, accommodation, tours and attractions, do ticketing, and process payments. Poor <1 year Services industries $48K-$55K per year, $55K-$100K per year
407 Telecommunications Technician Kaihangarau Torotoro Waea Telecommunications technicians install, maintain and repair electronic communications equipment in telecommunications networks and internet supply systems. Good 1-3 years Manufacturing and technology $48K-$75K per year, $75K-$95K per year
408 Upholsterer Kaiwhakapaipai Uhinga Waka Upholsterers make or repair the springs, padding, linings and covers of furniture. Average N/A Manufacturing and technology, Creative industries $48K-$50K per year, $50K-$62K per year
409 Animal Care Attendant Kaitiaki Kararehe Animal care attendants care for, and clean up after animals in places like kennels, pet shops and animal shelters. Average N/A Social and community services, Services industries, Primary industries $23 per hour, $25-$26 per hour
410 Zookeeper Kaitiaki Rawhi Whakaaturanga Zookeepers care for animals in zoos, wildlife parks and aquariums. Poor N/A Services industries $48K-$90K per year, $95K-$117K per year
411 Youth Worker Kaimahi Taiohi Youth workers support young people to improve their health, education, training and employment opportunities. Good 1-2 years Social and community services $48K-$60K per year, $58K-$74K per year
412 Wood Processing Worker Kaimahi Kani Rākau Wood processing workers set up and use woodworking machinery to cut logs into timber for building, furniture and other products. Average 1-2 years Manufacturing and technology, Primary industries $48K-$50K per year
413 Biosecurity Officer Δ€piha Ao Koiora Biosecurity officers check areas of land for harmful animals or plants and arrange for, or help with, pest destruction and control. Good 3 years Social and community services, Primary industries $65K-$80K per year, $100K per year
414 Hunter/​Trapper Kaikimi/​Kaihopu Kararehe Hunters and trappers shoot or trap animals to remove pests and control disease, and for food, fur, or research. Average N/A Primary industries
415 Naturopath Kaihaumanu Hauora Aro Tini Naturopaths diagnose and treat health problems with nutritional and lifestyle advice, herbal medicines and natural therapies. Poor 2-3 years Social and community services $48K-$120K per year
416 Dog Trainer Kaiako KurΔ« Dog trainers train dogs and diagnose and treat their behaviour problems. Poor N/A Primary industries, Services industries $48K-$50K per year
InΒ [95]:
grouped = df_jobprofiles.groupby(['Job Opportunities','Industry'])
grouped.groups
Out[95]:
{('Average', 'Construction and infrastructure'): [130, 175, 176, 200], ('Average', 'Construction and infrastructure, Creative industries'): [187], ('Average', 'Construction and infrastructure, Manufacturing and technology'): [136, 174, 258], ('Average', 'Construction and infrastructure, Manufacturing and technology, Creative industries'): [282, 283], ('Average', 'Construction and infrastructure, Primary industries'): [254], ('Average', 'Construction and infrastructure, Primary industries, Creative industries'): [182], ('Average', 'Construction and infrastructure, Services industries'): [0, 327], ('Average', 'Construction and infrastructure, Services industries, Manufacturing and technology'): [54], ('Average', 'Creative industries'): [51, 297, 330], ('Average', 'Manufacturing and technology'): [16, 48, 213, 215, 357], ('Average', 'Manufacturing and technology, Construction and infrastructure'): [251], ('Average', 'Manufacturing and technology, Creative industries'): [49, 346, 347, 348, 408], ('Average', 'Manufacturing and technology, Primary industries'): [312, 402, 412], ('Average', 'Manufacturing and technology, Services industries'): [77], ('Average', 'Manufacturing and technology, Services industries, Creative industries'): [244, 354], ('Average', 'Primary industries'): [19, 88, 109, 110, 139, 305, 338, 396, 414], ('Average', 'Primary industries, Services industries'): [21, 307, 355], ('Average', 'Primary industries, Social and community services'): [157], ('Average', 'Services industries'): [8, 22, 44, 53, 81, 93, 153, 180, 204, 207, 209, 229, 239, 252, 253, 284, 285, 314, 316, 319, 328, 332, 333, 334, 353, 363, 364, 371, 373, 387, 388, 389, 390, 403], ('Average', 'Services industries, Creative industries'): [80, 238, 294, 299, 308, 350, 397, 400], ('Average', 'Services industries, Manufacturing and technology'): [64, 318], ('Average', 'Services industries, Primary industries'): [181], ('Average', 'Services industries, Social and community services'): [105, 106, 108, 205, 303], ('Average', 'Social and community services'): [34, 103, 104, 113, 135, 140, 141, 142, 144, 149, 161, 196, 234, 237, 265, 280, 291, 367, 377], ('Average', 'Social and community services, Primary industries'): [191, 250], ('Average', 'Social and community services, Services industries'): [73], ('Average', 'Social and community services, Services industries, Primary industries'): [409], ('Good', 'Construction and infrastructure'): [2, 32, 36, 37, 45, 46, 50, 55, 62, 65, 134, 162, 163, 165, 202, 208, 220, 230, 231, 376, 405], ('Good', 'Construction and infrastructure, Creative industries'): [129, 255], ('Good', 'Construction and infrastructure, Manufacturing and technology'): [59, 128, 262, 289, 374, 375, 401], ('Good', 'Construction and infrastructure, Primary industries'): [232], ('Good', 'Construction and infrastructure, Primary industries, Manufacturing and technology'): [78], ('Good', 'Construction and infrastructure, Services industries'): [190], ('Good', 'Construction and infrastructure, Services industries, Manufacturing and technology'): [124], ('Good', 'Construction and infrastructure, Social and community services'): [186], ('Good', 'Manufacturing and technology'): [4, 23, 28, 47, 60, 66, 75, 76, 91, 95, 117, 119, 123, 127, 132, 133, 160, 272, 273, 275, 276, 277, 278, 279, 287, 288, 290, 302, 342, 345, 362, 379, 380, 407], ('Good', 'Manufacturing and technology, Construction and infrastructure'): [164, 222, 320], ('Good', 'Manufacturing and technology, Creative industries'): [349], ('Good', 'Manufacturing and technology, Primary industries'): [5, 96, 216, 313, 398], ('Good', 'Manufacturing and technology, Services industries'): [26, 94, 126, 211, 263, 321, 341], ('Good', 'Manufacturing and technology, Social and community services'): [292], ('Good', 'Primary industries'): [9, 13, 17, 20, 61, 111, 112, 152, 171, 172, 177, 178, 206, 214, 322, 323, 324, 337, 344, 368, 404], ('Good', 'Primary industries, Construction and infrastructure'): [52], ('Good', 'Primary industries, Manufacturing and technology'): [370], ('Good', 'Primary industries, Social and community services'): [31], ('Good', 'Services industries'): [6, 7, 24, 35, 38, 40, 42, 58, 63, 69, 70, 71, 74, 82, 89, 90, 97, 99, 100, 118, 166, 168, 169, 170, 179, 183, 194, 199, 217, 218, 223, 249, 256, 267, 300, 301, 309, 310, 311, 315, 329, 336, 356, 369, 381, 382, 391, 392, 399], ('Good', 'Services industries, Construction and infrastructure'): [351], ('Good', 'Services industries, Creative industries'): [79, 138, 335, 361], ('Good', 'Services industries, Manufacturing and technology'): [359], ('Good', 'Services industries, Social and community services'): [10, 107, 221, 264, 281, 304, 395], ('Good', 'Services industries, Social and community services, Primary industries'): [120, 167], ('Good', 'Social and community services'): [18, 30, 68, 83, 86, 92, 98, 101, 102, 116, 121, 122, 125, 131, 137, 143, 145, 146, 148, 154, 155, 156, 158, 159, 185, 192, 195, 201, 203, 224, 226, 227, 235, 240, 245, 247, 259, 261, 270, 271, 274, 306, 317, 325, 326, 331, 366, 383, 411], ('Good', 'Social and community services, Manufacturing and technology'): [84, 233], ('Good', 'Social and community services, Primary industries'): [413], ('Good', 'Social and community services, Services industries'): [147, 150], ('Poor', 'Construction and infrastructure'): [173], ('Poor', 'Construction and infrastructure, Manufacturing and technology'): [56], ('Poor', 'Creative industries'): [1, 11, 12, 14, 188, 197, 198, 210, 219, 241, 243, 260, 295, 298, 358, 385, 394], ('Poor', 'Manufacturing and technology'): [39, 212, 257, 384], ('Poor', 'Manufacturing and technology, Creative industries'): [286, 296, 340], ('Poor', 'Manufacturing and technology, Primary industries'): [386], ('Poor', 'Manufacturing and technology, Services industries'): [33], ('Poor', 'Manufacturing and technology, Social and community services'): [184], ('Poor', 'Primary industries, Services industries'): [416], ('Poor', 'Primary industries, Social and community services'): [151], ('Poor', 'Services industries'): [3, 25, 43, 85, 87, 228, 236, 246, 293, 339, 365, 372, 378, 393, 406, 410], ('Poor', 'Services industries, Construction and infrastructure'): [189], ('Poor', 'Services industries, Creative industries'): [41, 57, 193, 242, 343, 352, 360], ('Poor', 'Services industries, Social and community services'): [114, 268], ('Poor', 'Social and community services'): [15, 27, 67, 72, 225, 248, 266, 269, 415], ('Poor', 'Social and community services, Services industries, Creative industries'): [29, 115]}
InΒ [96]:
df_jobprofiles1=df_jobprofiles.iloc[grouped.groups['Good', 'Manufacturing and technology']]
df_jobprofiles1
Out[96]:
Occupation Sub Title Description Job Opportunities Training Required Industry Earnings
4 Electronics Engineer Mataaro Tāhiko Electronics engineers design and oversee production of electronic equipment such as radios, televisions, computers, washing machines and telecommunication systems. They may also work in sales and technical support. Good 4 years Manufacturing and technology $100K per year
23 Telecommunications Engineer Mataaro Whitiwhiti Kōrero Telecommunications engineers design, test and build telecommunications networks and systems. Good 2-4 years Manufacturing and technology $60K-$65K per year, $65K-$140K per year
28 Chemical Engineer Mataaro MatΕ« Chemical engineers design, develop and operate equipment and processes used to manufacture chemicals and products. Good 3-4 years Manufacturing and technology $100K per year
47 Plastics Technician Kaihangarau Kirihou Plastics technicians set up, adjust, maintain and repair machines that manufacture plastic products. Good 3 years Manufacturing and technology $23-$25 per hour, $25-$34 per hour
60 Glass Processor Kaiwhakarite Karaehe Glass processors prepare and process sheets of flat glass into products such as windows and mirrors for installation in buildings and related structures. Good 3 years Manufacturing and technology $23-$25 per hour, $23-$36 per hour
66 Recycler/​Dismantler Kaihangarua/​Kaiwetewete Recyclers/dismantlers take apart, separate, sort and sell materials to be recycled or reused. Good N/A Manufacturing and technology $23-$25 per hour
75 Automotive Electrician Kaimahi Hiko ā-Waka Automotive electricians install, maintain and repair electrical wiring, parts and electrical and electronic systems in vehicles. Good 3-4 years Manufacturing and technology $32-$45 per hour
76 Coachbuilder/​Trimmer Kaihanga Pahi/​Kaiwhakarākei Waka Coachbuilders manufacture and assemble frames, panels and parts for vehicles such as buses and motor homes. Vehicle trimmers install and repair the upholstery of vehicles. Good 3 years Manufacturing and technology $23 per hour, $23-$26 per hour
91 Electrical Engineer Mataaro PΕ«hiko Electrical engineers design, construct and manufacture electrical systems. They also maintain, operate and manage these systems. Good 4 years Manufacturing and technology $77K-$160K per year, $150K-$210K per year
95 Network Administrator Kaiwhakahaere Whatunga Network administrators design, install and maintain computer hardware and software networks, from one-building LANs (local area networks) to worldwide WANs (wide area networks). Good 1-3 years Manufacturing and technology $80K-$140K per year
117 Data Analyst Kaitātari Raraunga Data analysts identify and describe data trends using statistics and specialised software to help organisations achieve their business aims. Good 2-3 years Manufacturing and technology $90K-$120K per year, $110K-$170K per year
119 User Experience Designer Kaihoahoa Wheako Whakamahi User experience (UX) designersΒ design how products such as websites and apps look and work, based on users' needs. Good 1-3 years Manufacturing and technology $100K-$175K per year
123 Information Technology Architect Kaihoahoa Hangarau Pārongo Information technology (IT) architects analyse an organisation's IT needs, recommend solutions and oversee their delivery and implementation. Good 3-5 years Manufacturing and technology $140K-$200K per year
127 Systems Administrator Kaiwhakahaere PΕ«naha Systems administrators develop, maintain and administer computer operating systems, database management systems, and security policies and procedures. Good 2-4 years Manufacturing and technology $85K-$120K per year, $85K-$145K per year
132 Automotive Technician Kaihangarau PΕ«kaha Waka Automotive technicians service and repair vehicles and their parts and systems. Good 3-4 years Manufacturing and technology $25-$40 per hour, $26-$45 per hour
133 Automotive Refinisher Kaipeita Waka Automotive refinishers prepare vehicle surfaces, match and mix colours, and apply paint to vehicles. Good 3-4 years Manufacturing and technology $23-$28 per hour, $28-$38 per hour
160 Mechanical Engineer Mataaro PΕ«kaha Mechanical engineers design and give advice on the building and repair of machines and tools. They also investigate problems and faults with machinery, and study ways to improve manufacturing and energy production. Good 3-4 years Manufacturing and technology $85K-$105K per year, $75K-$180K per year
272 Business Analyst Kaitātari Pakihi Business analysts design or recommend solutions, such as computers or computer programs, to help organisations meet their goals. Good 2-3 years Manufacturing and technology $115K–$140K per year, $145K–$160K per year
273 Information Technology Helpdesk/​Support Technician Kaihangarau Δ€whina Hangarau Pārongo Information technology (IT) helpdesk/support technicians set up computer and IT equipment and identify and fix hardware and software problems Good 1-3 years Manufacturing and technology $60K-$90K per year, $90K-$120K per year
275 Security Analyst Kaitātari Whakamarumaru Security analysts create and monitor security processes and frameworks to protect an organisations information systems and computer networks from being illegally accessed. Good 1-4 years Manufacturing and technology $120K-$200K per year, $150K-$500K per year
276 Security Consultant Kaitohutohu Whakamarumaru Security consultants identify security weakness in information technology (IT), advise on IT security, and design IT security systems. Good 2-3 years Manufacturing and technology $120K-$200K per year, $180K-$500K per year
277 Test Analyst Kaitātari Whakamātautau Test analysts design and carry out tests for computer software and systems, analyse results, and identify and report problems. Good 1-3 years Manufacturing and technology $80K-$130K per year, $130K-$200K per year
278 Scrum Master Kaitakawaenga Kakari Scrum masters use the scrum software development approach to keep IT project teams on track. They also help remove obstacles to progress. Good 2-3 years Manufacturing and technology $120K-$200K per year
279 Penetration Tester Kaiwhakamātautau Mūrere Penetration testers investigate security weaknesses in online systems and databases. Good 1-4 years Manufacturing and technology $100K-$200K per year
287 Software Developer Kaihanga PΕ«manawa Rorohiko Software developers create and maintain computer software, websites and software applications (apps). Good 1-3 years Manufacturing and technology $110K- $160K per year
288 Electrical Engineering Technician Kaihangarau Take PΕ«hiko Electrical engineering technicians interpret the designs and technical instructions of electrical engineers, then ensure they are accurately carried out. They also develop, install, test and maintain equipment that produces, transmits or uses power. Good 2-3 years Manufacturing and technology $23-$27 per hour, $34-$43 per hour
290 Refrigeration/​Air-conditioning Technician Kaihangarau PΕ«rere Whakamātao Refrigeration/air-conditioning technicians install, service and maintain refrigeration and air-conditioning systems. Good 0-4 years Manufacturing and technology $50K-$95K per year
302 Aircraft Maintenance Engineer Mataaro Whakatika Waka Rererangi Aircraft maintenance engineers keep aeroplanes safe. They install, inspect, maintain and repair aircraft, aircraft radio, avionic (electronic), navigation, communication and electrical and mechanical systems. Good 3-5 years Manufacturing and technology $48K-$91K per year, $86K-$165K per year
342 Engineering Machinist Kaiwhakamahi PΕ«rere Engineering machinists create, assemble and repair metal products by interpreting designs, measuring metals, and operating machines to cut and shape them. Good 4 years Manufacturing and technology $48K per year, $48K-$103K per year
345 Electronics Trades Worker Ringarehe Tāhiko Electronics trades workers assemble, install and fix electronic parts and equipment. Good 3-4 years Manufacturing and technology $48K-$80K per year, $75K-$120K per year
362 Mechanical Engineering Technician Kaihangarau Take PΕ«kaha Mechanical engineering technicians help mechanical engineers design, develop, test and manufacture mechanical devices, including tools, engines and machines. Good 2-4 years Manufacturing and technology $48K-$65K per year, $85K-$140K per year
379 Printer Kaiwhakamahi Mīhini Tā Printers set up and operate printing machines that print text and images on paper, card, carton, plastics or metal. Good 2-3 years Manufacturing and technology $48K-$50K per year, $62K-$93K per year
380 Print Finisher Kaiwhakatau Tānga Print finishers bind, finish and repair books and other publications by hand or by machine. Good 3 years Manufacturing and technology $50K-$65K per year
407 Telecommunications Technician Kaihangarau Torotoro Waea Telecommunications technicians install, maintain and repair electronic communications equipment in telecommunications networks and internet supply systems. Good 1-3 years Manufacturing and technology $48K-$75K per year, $75K-$95K per year

3.7.2 Transfer the earnings column from text to number.ΒΆ

InΒ [97]:
df = pd.DataFrame(df_jobprofiles1)

def split_earnings(earnings):
    earnings = earnings.replace("K", "000").replace("per hour", "").replace("per year", "").replace("$", "").strip()
    earnings = earnings.replace("βˆ’", "-") 
    try:
        if "," in earnings:
            parts = earnings.split(",")
            min_earning = float(parts[0].split("-")[0].strip())
            max_earning = float(parts[1].split("-")[1].strip())
        elif "-" in earnings:
            parts = earnings.split("-")
            min_earning = float(parts[0].strip())
            max_earning = float(parts[1].strip())
        else:
            min_earning = max_earning = float(earnings.strip())
    except Exception as e:
        print(f"Error processing earnings '{earnings}': {e}")
        min_earning = max_earning = None
    return min_earning, max_earning

df[['Min Earnings', 'Max Earnings']] = df['Earnings'].apply(split_earnings).apply(pd.Series)
df
Error processing earnings '115000–140000 , 145000–160000': could not convert string to float: '115000–140000'
Out[97]:
Occupation Sub Title Description Job Opportunities Training Required Industry Earnings Min Earnings Max Earnings
4 Electronics Engineer Mataaro Tāhiko Electronics engineers design and oversee production of electronic equipment such as radios, televisions, computers, washing machines and telecommunication systems. They may also work in sales and technical support. Good 4 years Manufacturing and technology $100K per year 100000.0 100000.0
23 Telecommunications Engineer Mataaro Whitiwhiti Kōrero Telecommunications engineers design, test and build telecommunications networks and systems. Good 2-4 years Manufacturing and technology $60K-$65K per year, $65K-$140K per year 60000.0 140000.0
28 Chemical Engineer Mataaro MatΕ« Chemical engineers design, develop and operate equipment and processes used to manufacture chemicals and products. Good 3-4 years Manufacturing and technology $100K per year 100000.0 100000.0
47 Plastics Technician Kaihangarau Kirihou Plastics technicians set up, adjust, maintain and repair machines that manufacture plastic products. Good 3 years Manufacturing and technology $23-$25 per hour, $25-$34 per hour 23.0 34.0
60 Glass Processor Kaiwhakarite Karaehe Glass processors prepare and process sheets of flat glass into products such as windows and mirrors for installation in buildings and related structures. Good 3 years Manufacturing and technology $23-$25 per hour, $23-$36 per hour 23.0 36.0
66 Recycler/​Dismantler Kaihangarua/​Kaiwetewete Recyclers/dismantlers take apart, separate, sort and sell materials to be recycled or reused. Good N/A Manufacturing and technology $23-$25 per hour 23.0 25.0
75 Automotive Electrician Kaimahi Hiko ā-Waka Automotive electricians install, maintain and repair electrical wiring, parts and electrical and electronic systems in vehicles. Good 3-4 years Manufacturing and technology $32-$45 per hour 32.0 45.0
76 Coachbuilder/​Trimmer Kaihanga Pahi/​Kaiwhakarākei Waka Coachbuilders manufacture and assemble frames, panels and parts for vehicles such as buses and motor homes. Vehicle trimmers install and repair the upholstery of vehicles. Good 3 years Manufacturing and technology $23 per hour, $23-$26 per hour 23.0 26.0
91 Electrical Engineer Mataaro PΕ«hiko Electrical engineers design, construct and manufacture electrical systems. They also maintain, operate and manage these systems. Good 4 years Manufacturing and technology $77K-$160K per year, $150K-$210K per year 77000.0 210000.0
95 Network Administrator Kaiwhakahaere Whatunga Network administrators design, install and maintain computer hardware and software networks, from one-building LANs (local area networks) to worldwide WANs (wide area networks). Good 1-3 years Manufacturing and technology $80K-$140K per year 80000.0 140000.0
117 Data Analyst Kaitātari Raraunga Data analysts identify and describe data trends using statistics and specialised software to help organisations achieve their business aims. Good 2-3 years Manufacturing and technology $90K-$120K per year, $110K-$170K per year 90000.0 170000.0
119 User Experience Designer Kaihoahoa Wheako Whakamahi User experience (UX) designersΒ design how products such as websites and apps look and work, based on users' needs. Good 1-3 years Manufacturing and technology $100K-$175K per year 100000.0 175000.0
123 Information Technology Architect Kaihoahoa Hangarau Pārongo Information technology (IT) architects analyse an organisation's IT needs, recommend solutions and oversee their delivery and implementation. Good 3-5 years Manufacturing and technology $140K-$200K per year 140000.0 200000.0
127 Systems Administrator Kaiwhakahaere PΕ«naha Systems administrators develop, maintain and administer computer operating systems, database management systems, and security policies and procedures. Good 2-4 years Manufacturing and technology $85K-$120K per year, $85K-$145K per year 85000.0 145000.0
132 Automotive Technician Kaihangarau PΕ«kaha Waka Automotive technicians service and repair vehicles and their parts and systems. Good 3-4 years Manufacturing and technology $25-$40 per hour, $26-$45 per hour 25.0 45.0
133 Automotive Refinisher Kaipeita Waka Automotive refinishers prepare vehicle surfaces, match and mix colours, and apply paint to vehicles. Good 3-4 years Manufacturing and technology $23-$28 per hour, $28-$38 per hour 23.0 38.0
160 Mechanical Engineer Mataaro PΕ«kaha Mechanical engineers design and give advice on the building and repair of machines and tools. They also investigate problems and faults with machinery, and study ways to improve manufacturing and energy production. Good 3-4 years Manufacturing and technology $85K-$105K per year, $75K-$180K per year 85000.0 180000.0
272 Business Analyst Kaitātari Pakihi Business analysts design or recommend solutions, such as computers or computer programs, to help organisations meet their goals. Good 2-3 years Manufacturing and technology $115K–$140K per year, $145K–$160K per year NaN NaN
273 Information Technology Helpdesk/​Support Technician Kaihangarau Δ€whina Hangarau Pārongo Information technology (IT) helpdesk/support technicians set up computer and IT equipment and identify and fix hardware and software problems Good 1-3 years Manufacturing and technology $60K-$90K per year, $90K-$120K per year 60000.0 120000.0
275 Security Analyst Kaitātari Whakamarumaru Security analysts create and monitor security processes and frameworks to protect an organisations information systems and computer networks from being illegally accessed. Good 1-4 years Manufacturing and technology $120K-$200K per year, $150K-$500K per year 120000.0 500000.0
276 Security Consultant Kaitohutohu Whakamarumaru Security consultants identify security weakness in information technology (IT), advise on IT security, and design IT security systems. Good 2-3 years Manufacturing and technology $120K-$200K per year, $180K-$500K per year 120000.0 500000.0
277 Test Analyst Kaitātari Whakamātautau Test analysts design and carry out tests for computer software and systems, analyse results, and identify and report problems. Good 1-3 years Manufacturing and technology $80K-$130K per year, $130K-$200K per year 80000.0 200000.0
278 Scrum Master Kaitakawaenga Kakari Scrum masters use the scrum software development approach to keep IT project teams on track. They also help remove obstacles to progress. Good 2-3 years Manufacturing and technology $120K-$200K per year 120000.0 200000.0
279 Penetration Tester Kaiwhakamātautau Mūrere Penetration testers investigate security weaknesses in online systems and databases. Good 1-4 years Manufacturing and technology $100K-$200K per year 100000.0 200000.0
287 Software Developer Kaihanga PΕ«manawa Rorohiko Software developers create and maintain computer software, websites and software applications (apps). Good 1-3 years Manufacturing and technology $110K- $160K per year 110000.0 160000.0
288 Electrical Engineering Technician Kaihangarau Take PΕ«hiko Electrical engineering technicians interpret the designs and technical instructions of electrical engineers, then ensure they are accurately carried out. They also develop, install, test and maintain equipment that produces, transmits or uses power. Good 2-3 years Manufacturing and technology $23-$27 per hour, $34-$43 per hour 23.0 43.0
290 Refrigeration/​Air-conditioning Technician Kaihangarau PΕ«rere Whakamātao Refrigeration/air-conditioning technicians install, service and maintain refrigeration and air-conditioning systems. Good 0-4 years Manufacturing and technology $50K-$95K per year 50000.0 95000.0
302 Aircraft Maintenance Engineer Mataaro Whakatika Waka Rererangi Aircraft maintenance engineers keep aeroplanes safe. They install, inspect, maintain and repair aircraft, aircraft radio, avionic (electronic), navigation, communication and electrical and mechanical systems. Good 3-5 years Manufacturing and technology $48K-$91K per year, $86K-$165K per year 48000.0 165000.0
342 Engineering Machinist Kaiwhakamahi PΕ«rere Engineering machinists create, assemble and repair metal products by interpreting designs, measuring metals, and operating machines to cut and shape them. Good 4 years Manufacturing and technology $48K per year, $48K-$103K per year 48000.0 103000.0
345 Electronics Trades Worker Ringarehe Tāhiko Electronics trades workers assemble, install and fix electronic parts and equipment. Good 3-4 years Manufacturing and technology $48K-$80K per year, $75K-$120K per year 48000.0 120000.0
362 Mechanical Engineering Technician Kaihangarau Take PΕ«kaha Mechanical engineering technicians help mechanical engineers design, develop, test and manufacture mechanical devices, including tools, engines and machines. Good 2-4 years Manufacturing and technology $48K-$65K per year, $85K-$140K per year 48000.0 140000.0
379 Printer Kaiwhakamahi Mīhini Tā Printers set up and operate printing machines that print text and images on paper, card, carton, plastics or metal. Good 2-3 years Manufacturing and technology $48K-$50K per year, $62K-$93K per year 48000.0 93000.0
380 Print Finisher Kaiwhakatau Tānga Print finishers bind, finish and repair books and other publications by hand or by machine. Good 3 years Manufacturing and technology $50K-$65K per year 50000.0 65000.0
407 Telecommunications Technician Kaihangarau Torotoro Waea Telecommunications technicians install, maintain and repair electronic communications equipment in telecommunications networks and internet supply systems. Good 1-3 years Manufacturing and technology $48K-$75K per year, $75K-$95K per year 48000.0 95000.0

Based on the data from the website, earnings include both hourly and annual salaries. In this study, we will not consider the hourly wage part. After converting the text to numerical values, we categorize the earnings into minimum and maximum values. The next step will involve further analysis.

3.7.3 Visualize the maximum yearly salary by occupation and training years in the manufacturing and technology industries.ΒΆ

InΒ [98]:
df = pd.DataFrame(df_jobprofiles1)

def split_earnings(earnings):
    earnings = earnings.replace("K", "000").replace("per hour", "").replace("per year", "").replace("$", "").strip()
    earnings = earnings.replace("βˆ’", "-")  
    try:
        if "," in earnings:
            parts = earnings.split(",")
            min_earning = float(parts[0].split("-")[0].strip())
            max_earning = float(parts[1].split("-")[1].strip())
        elif "-" in earnings:
            parts = earnings.split("-")
            min_earning = float(parts[0].strip())
            max_earning = float(parts[1].strip())
        else:
            min_earning = max_earning = float(earnings.strip())
    except Exception as e:
        print(f"Error processing earnings '{earnings}': {e}")
        min_earning = max_earning = None
    return min_earning, max_earning

df[['Min Earnings', 'Max Earnings']] = df['Earnings'].apply(split_earnings).apply(pd.Series)
df_filtered = df[df['Max Earnings'] >= 5000]

plt.figure(figsize=(10, 6))
plt.barh(df_filtered['Occupation'], df_filtered['Max Earnings'], color='fuchsia')
plt.xlabel('Maximum Earnings')
plt.ylabel('Occupation')
plt.title('Maximum Earnings by Occupation (Yearly Salary) ')
Error processing earnings '115000–140000 , 145000–160000': could not convert string to float: '115000–140000'
Out[98]:
Text(0.5, 1.0, 'Maximum Earnings by Occupation (Yearly Salary) ')
No description has been provided for this image

The highest yearly salary can be observed for security consultants and security analysts, at around 500,000. πΉπ‘œπ‘Ÿ π‘‘π‘Žπ‘‘π‘Ž π‘Žπ‘›π‘Žπ‘™π‘¦π‘ π‘‘π‘ , π‘‘β„Žπ‘’ π‘šπ‘Žπ‘₯π‘–π‘šπ‘’π‘š π‘¦π‘’π‘Žπ‘Ÿπ‘™π‘¦ π‘ π‘Žπ‘™π‘Žπ‘Ÿπ‘¦ 𝑖𝑠 π‘Žπ‘π‘π‘Ÿπ‘œπ‘₯π‘–π‘šπ‘Žπ‘‘π‘’π‘™π‘¦ 170,000. According to the website's definition, these highest salaries correspond to more senior or advanced positions.

InΒ [99]:
df = pd.DataFrame(df_jobprofiles1)

def split_earnings(earnings):
    earnings = earnings.replace("K", "000").replace("per hour", "").replace("per year", "").replace("$", "").strip()
    earnings = earnings.replace("βˆ’", "-") 
    try:
        if "," in earnings:
            parts = earnings.split(",")
            min_earning = float(parts[0].split("-")[0].strip())
            max_earning = float(parts[1].split("-")[1].strip())
        elif "-" in earnings:
            parts = earnings.split("-")
            min_earning = float(parts[0].strip())
            max_earning = float(parts[1].strip())
        else:
            min_earning = max_earning = float(earnings.strip())
    except Exception as e:
        print(f"Error processing earnings '{earnings}': {e}")
        min_earning = max_earning = None
    return min_earning, max_earning

df[['Min Earnings', 'Max Earnings']] = df['Earnings'].apply(split_earnings).apply(pd.Series)

df_filtered = df[df['Max Earnings'] >= 5000]

df_sorted = df_filtered.sort_values(by='Max Earnings', ascending=False)

plt.figure(figsize=(10, 6))
plt.barh(df_sorted['Training Required'], df_sorted['Max Earnings'], color='indigo')
plt.xlabel('Maximum Earnings')
plt.ylabel('Training Required')
plt.title('Maximum Earnings by Training Required (Yearly Salary) ')
Error processing earnings '115000–140000 , 145000–160000': could not convert string to float: '115000–140000'
Out[99]:
Text(0.5, 1.0, 'Maximum Earnings by Training Required (Yearly Salary) ')
No description has been provided for this image

From this table, we can see the relationship between maximum earnings and different training years. Since the value of years is defined as open-ended on the website, the results show various combinations. According to the displayed values, training years and maximum wage are not always directly proportional. Instead, maximum earnings are more closely related to the occupation.

SummaryΒΆ

3.1 Discuss the overall GDP changes in New Zealand from 2010 to 2022. What are the top three industrial contributions?

The top three contributing industries are Professional, Scientific, and Technical Services, Manufacturing, and Health Care and Social Assistance.

3.2 Does the industry that contributes the most GDP bring the same proportion of job opportunities?

Industries contributing the most to GDP do not provide the most jobs.

3.3 What are the job opportunities in the six main industries based on the occupation listed on Careers.NZ?

Manufacturing and Technology sectors have the best prospects, Construction and Infrastructure also fare well, while the Creative Industries face poor prospects.

3.4 As the Largest City in New Zealand and a Major Economic Hub, is Auckland the Best in Terms of All Vacancy Indices (AVIs) and GDP from 2010 to 2022?

Auckland has the highest GDP contribution but not the highest AVIs, with regions like Bay of Plenty and Otago Southland often showing higher AVI values.

3.5 How Does Unemployment Rate in New Zealand Relate to Auckland's AVI and GDP?

High unemployment correlates with lower AVIs and vice versa. Auckland's GDP improvement correlates with lower unemployment rates over time.

3.6 Does the Job Creation in Auckland from 1999 to 2022 Remain Positive, Making Auckland the City with the Most Economic Contribution?

From 1999 to 2022, Auckland's job market showed a generally positive trend, with job creation consistently outpacing job destruction. However, there were a couple of exceptions, notably in 1999 and 2019, where more jobs were lost than created. These dips could be attributed to specific economic policies or significant events like the COVID-19 pandemic in 2019.

3.7 What are the Employment Prospects and Highest Salary Ranges for the Manufacturing and Technology Industries, as well as for Data Analyst Positions?

The highest yearly salaries in the manufacturing and technology industries, particularly for senior roles such as security consultants and security analysts, can reach up to 500,000. For data analysts, the maximum yearly salary is approximately 170,000. According to the data, maximum earnings are more closely related to the specific occupation rather than the number of training years, indicating that higher salaries are often associated with more senior or advanced positions within these industries.

ConclusionΒΆ

The use of API retrieval and web scraping has elevated the scope of our analysis beyond static documents. This process allows us to incorporate information from multiple perspectives, providing a comprehensive analysis of New Zealand's GDP changes, industry contributions, job opportunities, and salary ranges from 2010 to 2022. It is evident that New Zealand's GDP has grown steadily over the years, with minimal fluctuations between industries.

Despite the uncertainties in global markets and changes in domestic policies that have increased volatility in the employment market, the technology and manufacturing sectors have shown significant growth potential, particularly for senior positions. Auckland, as an economic hub, leads in GDP contribution but has room for improvement in vacancy indices compared to other cities. The technology industry is a major pillar of New Zealand's economy, and our field of study(Master of Analytics) shows optimistic and promising trends in the overall market. Overall, understanding these dynamics is key to taking advantage of opportunities and addressing challenges in New Zealand's ever-changing job market.